Multimodal Transportation Planning Data: Final Report (1997)

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Jack Faucett Associates. Inc. Final Report 2. FINDINGS March 1997 The progression toward multimodalism, supported and strengthened by ISTEA and the 1 990 CAAA, provide an impetus for change in transportation planning and project implementation. As a result, all levels of government are now confronted with a rapidly changing focus and set of constraints as they provide mobility for both people and goods. The changing focus has added considerable complexity and data needs to the planning process. In this study, multimodal transportation planning is defined to include activities undertaken by MPOs and state-DOTs that ensure equilibrium between transport demand and supply consistent with other societal goals. Multimodal transportation planning strategies that strive to accomplish the following objectives. Anticipate and manage growth in travel demand stemming from changes in the economic and demographic composition of a region, known as secular growth. Improve and better manage system performance (e.g., decrease congestion on highways). For example, strategies such as ramp metering decrease congestion downstream along the equipped freeway, and decreases in congestion increase level-of-service along the Leeway. Preserve existing system to ensure integrity. This planning activity ensures that supply does not deteriorate to levels that increase user costs. Expand system to meet secular growth in demand. This planning strategy represents traditional capacity expansion projects that have been the focus of highway planning in recent decades. Although ISTEA calls for integrated and cooperative planning between MPOs and state-DOTs, various planning activities will be undertaken independently by either MPOs and state-DOTs. For example, travel modeling and forecasting is predominantly conducted by MPOs, although some states do develop, calibrate, and execute travel models. This planning activity is integral to the demand side of transportation planning as defined above. Likewise, strategies to mitigate traffic congestion and to promote shifts from single occupancy vehicles to high occupancy vehicles (e.g., transit) are predominantly developed, implemented, and evaluated by MPOs since such strategies address local transportation problems. On the other hand, state-DOTs are predominantly responsible for planning strategies that address system supply, either via expansion projects or system maintenance. Consequently, the definition of multimodal transportation planning facilitates the identification of differences in planning activities conducted by state-DOTs and MPOs. Differences in planning responsibilities are important from the perspective of assessing data needs. However, the development of a comprehensive, integrated data program should transcend jurisdictions so that all users can access information from a centralized source to meet their planning needs. (Data NCHRP Multimodal Transportation 11 Planning Data Project 8-32(5)

Lack Faucett Associates Final Report March 1997 - integration is the subject of Task 6). The definition of planning stated above is employed below to assess data reeds separately for MPOs and state-DOTs. 2.l Task I: Strategic Assessment of Data Needs The definition of multimodal transportation planning provided above encompasses a wide range of planning missions, oh jectives, and strategies imbedded in MPO and state-nOT nl~nnin(, In order LU J USt1~ me selection or a given strategy to support the planning mission and objectives, MPOs and state-DOTs need to collect arid analyze data that describe the strateov'.s imnn~.t On try APmanA am system supply. ~_ 1 _ ~^1 1 ~, , . . .. ~. ~_ c,. ~J _ _ ~ ~ At __ ~ ^^ ~^ ~ ~ ~.1~l ~1~ This section presents the results of a systematic, strategic assessment of multimodal transportation planning data needs stemming from the progression toward multimodalism. Section 2.1.1 provides an overview of state-DOT and MPO planning requirements generated by multimodalism. The purpose of Section 2.1.1 is to define the planning requirements that are driving the need for more expansive and detailed data. Section 2.1.2 reviews strategic planning models that can be employed to assess data needs and describes the model chosen for this study (i.e., the Business Model). Finally, Section 2.1.3 applies the Business Model to assess selected state-DOT and MPO multimodal transportation planning data needs. 2.1.1 Overview of Planning and Information Requirements GSTEA and the 1990 CAAA! Multimodalism, ISTEA, and the 1990 CAAA are generating major revisions in the process and products of transportation planning conducted by state-DOTs and MPOs. The 1990 CAAA sets forth specific data collection, analysis, and reporting requirements for regions not in attainment of national air quality standards for ozone, carbon monoxide, and particulate matter. ISTE:A sets forth specific requirements for metropolitan planning and statewide planning and calls for programs at the state-DOT and MPO levels to manage data systems in a comprehensive and integrated manner. This sub-section summarizes the impact of ISTEA and the 1 990 CAAA on metropolitan and statewide planning and data collection and analysis. The goal is to develop an analytical baseline for a strategic assessment of data needs.3 2.1.1.1 ISTEA Planning Requirements As articulated in ISTEA, the new mission of the transportation planning process is as follows: 3For a more detailed overview of data requirements stemming from Federal legislation see: U.S. Department of Transportation, Identification of Trarlsporfatiorl Planning Data Requirements in Federal Legislation, DOT-T-94- 21, July 1994. NCHRP-Multimodal Transportation Planning Data 12 Project 8-32~5)

Jack Fa~ccettAssoci'~es, Ine. Final Report March 1997 ...to develop a National Intermodal Transportation System that is economically efficient, environmentally sound, provide* the foundation to compete in the global economy and will move people and goods in an energy efficient manner. The recent six mandated management systems under ISTEA have been suspended under the National Highway System Designation Act and states may elect not to implement any of the six management systems. The six systems are described below with the mandated terminology deleted. Later references to the previously mandated six systems are now described as suggestions or as elective. ISTEA 's Ma~zageme'2t Systems-The term "management system" implies a systematic process designed to assist transportation planners and decision makers in selecting cost-effective planning strategies to improve the efficiency and safety of the transportation infrastructure. Management systems represent objectives that the planning process should strive to satisfy. Specific strategies to support the development and implementation of management systems constitute chosen transportation decisions designed to optimize the efficiency of the transportation system. Data are suggested to develop a management system and to also design, evaluate, and implement strategies supporting each management system. Exhibit 2 depicts the relationship he.tween Ohio mana~-m~nt systems and state-DOT and MPO transportation planning. ~~-r ~- .,~ _ The ISTEA management systems require data to define and monitor the magnitude of transportation system problems, to analyze alternative solutions, and to measure the effectiveness of the implemented strategies. Some data needs, such as traffic volumes or travel demand, may be common to each of the six systems, while other data requirements will be unique to specific system strategies. The principal types of date needed for the development and implementation ofthe ISTEA management systems and supporting strategies for traffic monitoring and control include: traffic counting programs, travel time surveys, home interview surveys, employer surveys, vehicle occupancy counts, screen line counts, travel behavior studies, surveys at activity centers, parking inventories, site impact studies, cordon surveys, on-board transit surveys, and others. Much of the traffic data required for strategy development and implementation under a CMS, IMS, and/or PTMS, originates Mom the Traffic Monitoring Systems (TMS) deployed by MPOs. Data to be included in the TMS originates Mom continuous traffic counts, short-term ~aff~c monitoring, and vehicle occupancy monitoring. Typical data elements regarding traffic volume include: annual average daily traffic, design hourly volume, peak hour traffic percentage, peak period volume, and VMT. It is envisioned that congestion management will provide the most comprehensive information for planning since the CMS is required to continuously collect arid monitor data in order to determine the duration arid magnitude of congestion. Actual data to be collected depends on the performance measures that are selected to assess congestion and to estimate the chance in Win `'uh~n proposed mitigation strategies are implemented. ~_ ·=,_~~-an,- ,, ·-en NCHRP-Multimodal Transportation 13 Project 8-32(5) Planning Data

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Jack Faucett Associates, Inc. Final ReFon AIarc1l 1997 The PTMS will identify and evaluate strategies related to improving the efficiency of public transportation. Besides a comprehensive inventory of facilities, the PTMS will collect data on the number of vehicles and ridership for dedicated right-of-way at the maximum load points in the peak direction and for the daily time period. Likewise, the IMS expands the identification and evaluation of strategies for improving the efficiency of intermodal transportation. As such, the IMS involves the identification of performance measures to determine the efficiency of intermodal facilities and systems. The data collection and monitoring aspects of the IMS include a base year inventory of the physical and operating characteristics of facilities and a survey of the facilities to assess performance. The other management systems that states may implement in collaboration with MPOs include the Pavement Management System (PMS), the Bridge Management System (BMS), and the Highway Safety Management System (SMS). The development and implementation of these management systems also requires a systematic, comprehensive, and integrated approach to data collection, storage, distribution, and analysis. The PMS requires data on the following: the physical pavement features, including the number of lanes, length, width, surface, type, functional classification, and shoulder information; pavement- related transportation projects such as the project dates and types of construction, reconstruction, rehabilitation, and preventive maintenance; pavement condition such as ride, distress, rutting, and surface friction; and system usage such as traffic volumes, vehicle classifications, and system loads. The BMS must contain a data base and an ongoing program to collect data on bridge inventories, bridge inspections, and bridge maintenance costs. The SMS involves the coordination of safety programs such as motor carrier, corridor, and community-based traffic safety activities into a comprehensive approach to ensuring highway safet.v that accounts for the identification of hazardous conditions and the needs of special user groups (e.g., older drivers, pedestrians, bicyclists, etc.). Data required by the SMS includes information pertaining to crashes, traffic, pedestrians, enforcement, vehicles, bicyclists, drivers, highways, and medical services. MPO Long Range Plans-An MPO's transportation plan must have a 20-year horizon, and must "include both long- and short-range strategies/actions that lead to the development of an integrated intermodal metropolitan transportation system that facilitates the efficient movement of people and goods." There are several specific considerations that must be included in the plan, some of which imply the development of new planning paradigms and supporting data. First, an MPO's transportation plan must identify the demand for transportation services originating from the need to move both individuals and goods. Second, it must identify congestion management strategies that demonstrate a systematic approach to addressing current and future transportation demand. Third, the plan must identify pedestrian walkway and bicycle transportation facilities and assess the demand for these facilities. NCHR~-Multimodal Transportation 15 Planning Data Project 8-32~5)

Jack F'zuceu Associates, . . Final Renoir March 1997 Fourth, the plan must assess capital investment requirements and other measures to preserve and most efficiently use the existing system. Fifth, it must describe existing and proposed transportation facilities in nonattainment areas to permit conformity determinations. Sixth, it must include a multimodal evaluation of the transportation, socio-economic, environmental, and financial impacts of the overall plan. · Seventh, it must reflect the area's comprehensive long-range land use plan and metropolitan development goals. · Finally, an MPO's transportation plan must present a financial plan that demonstrates the consistency of proposed transportation investments with known and projected sources of revenue. ISTEA does not specify the models that should be employed to develop an MPO's transportation plan that accounts for the considerations described above, nor does it specify the types of data that are required to support planning strategies. It is envisioned, however, that the majority of the information required to support the metropolitan transportation plan will come from three of the six ISTEA management systems that may be implemented by the state and the MPo.4 Specifically, results from the implementation of a Congestion Management System (CMS), Intermodal Management System (IMS), and Public Transportation Facilities and Equipment Management System (PTMS) win have direct relationships to the development, implementation, and evaluation of planning strategies since they will identify transportation needs to ensure system efficiency. In turn, data for these three management systems largely will originate from an area's Traffic Monitoring System, household and business trip generation data, and forecasts of land use and socio-economic characteristics. MPO TIPs-The development of an MPO's TIP has a different focus than~atofthemetropolitar~ transportation plan. At a minimum, an MPO's TIP must cover a 3-year period arid must identify all transportation projects within the area proposed for Federal Finding. Only projects that are consistent with the transportation plan cart be included in the TIP. For informational purposes and air quality analysis all regionally significant projects to be fielded with non-Federal fiends also must be addressed in the TIP. 4Section 1034 of the ISTEA describes regulations that were to be issued for the development, establishment, and implementation by the State of the six management systems. However, ISTEA also specified the designation of urbanized areas win populations of over 200,000 as Transportation Management Areas (TMAs), that must develop, establish, and implement a CMS. Furthermore, ISTEA recommends that all MPOs consider We implementation of the six management systems. NCHRP Multimodal Transportation Planning Data . 16 Project 8-32(5)

.lack Faucett Associate.', Inc. Final Report March 1997 - As with the metropolitan transportation plan, the planning and data requirements for the TIP are not explicitly stated by ISTEA. However, a review of the various elements comprising a TIP indicates the types of data that may be necessary to support its development, especially with respect to conformity determination. For example, in nonattainment areas and TMAs, the TIP must provide the following data: air quality impacts for each proposed project within a nonattainment area; · list of projects from previous TIPs that were found to conform with the area's SIP and that are now part of the base case for conformity analysis; and · a list of projects that are proposed as required TCMs. In addition, TIPs must also include data on project cost, funding sources, and performance information on projects proposed in previous TIPs that were implemented. State-DOT Long Range Plans-Statewide transportation plans must cover a 20-year period, must be intermodal in focus, must contain a plan for non-motorized transportation, must be coordinated with metropolitan transportation plans, must contain information on short-range planning and policy studies, and must provide data on the availability of resources to carry out the plan. Furthermore, the plan must be coordinated with the metropolitan transportation plan. State-DOT STIPs-Requirements for the STIP are very similar to those for the TIP. First, the STIP must contain a list of transportation projects to be carried out in the first three years of the STIP. Metropolitan planning area TIPs must be included without modification after being approved by the MPO and the Governor. Second, the STIP must contain only projects consistent with the statewide plan. Third, in nonattainment areas and TMAs, the STIP must contain only those transportation projects found to conform to the conformity regulations. Fourth, the STIP must be financially constrained and must include information to demonstrate that funds can be expected to be available for project implementation. This requirement implies that the STIP must contain a list of funding sources. Summary of MPO and Sfate-DOT Information Requirements-The planning requirements discussed above imply important changes with respect to the tools and information that are employed by transportation planners. Some of the data needs for multimodal planning efforts include the following: data from traffic data analysis including data from HPMS and the Traffic Monitoring System; data resulting from arty management systems identifying statewide transportation needs, including data on physical facilities and system performance; NCHRP Multimodal Transportation 17 Planning Data Project 8-32~5)

J ck Faucef.t Associates, Inc. Final Re.port data on bicycle and pedestrian tripmaking; data on recreational travel and tourism; March 1997 data on the social, economic, energy, and environmental effects of transportation decisions; land use projection data including economic, demographic, environmental, growth management, and land use activities; financial data for plans and programs; . . data on existing and potential rights-of-ways for future transportation; and data on commercial motor vehicle efficiency. The development of TIPs and STIPs requires detailed data on the travel and traffic impacts of projects for the purpose of conformity determination. This is particularly the responsibility of MPOs, since STIPs are a compilation of metropolitan area TIPs. The data needed for conformity goes beyond the traditional characterization of spatial travel and traffic relationships to spatial and temporal relationships. Data needs associated with the 1 990 CAAA are reviewed below. 2.~.~.2 1990 CAAA Requirements Related to Transportation Planning The 1 990 CAAA stipulate various requirements that directly impact the types of analyses that must be conducted by transportation planners, predominantly at the MPO level. First, all ozone and carbon monoxide nonattainment areas are required to develop base year emission inventories characterizing the contribution of transportation sources (i.e., motor vehicles, locomotives, and watercraft) to the air pollution problem in the region as defined by the nonattainment boundary. Second, depending on the classification of nonattainment (i.e., moderate, serious, severe, extreme), those areas with the worst air pollution are required to implement transportation control measures whose purpose are to reduce growth in tripmaking and VMT and, thereby, mitigate the contribution of motor vehicle travel to future emissions within the region. Finally, all nonattainment areas are required to demonstrate conformity between transportation and air quality plans, as described in the introduction to this report. Emissions Inventories and Emissions Budgets- An emissions inventory is a current, comprehensive, and accurate inventory of actual weekday emissions for the applicable season (i.e., CO or ozone) from all sources in the nonattainment area, including a 25-mile radius beyond the nonattainment area boundary as required by EPA guidance. The development of emission inventories for transportation-related sources is the responsibility of both MPOs and a state's department of environmental quality. MPOs usually are responsible for developing emission NCHRP-Multimodal Transportation 18 Planning Data Project 8-32~5)

"rack Fauceu Associates, Inc. Final Report A,larcn 1997 estimates for on-road sources, while the emission contributions of nonroad transportation-related sources are developed at the state level. On-road sources are defined to include virtually all types of motor vehicles that operate on highways and roads across the country, and that provide for the transport of both passengers and freight. This definition includes: 1) passenger cars, usually referred to as light-duty gasoline vehicles; 2) pick-up trucks, often broken down into a) light duty gasoline trucks up to 6,000 pounds of gross vehicle weight (GVW), b) light duty gasoline trucks between 6,001 and 8,500 GVW, and c) light duty diesel trucks up to 8,500 GVW; 3) trucks, broken down into heavy duty gasoline vehicles rated at GVWs above 8,500 pounds and heavy duty diesel vehicles rated above 8,500 GVW; and 4) motorcycles. However, on-road motor vehicles are not the only modes of transportation that contribute to air pollution. Locomotives, airplanes, and watercraft are also sources of emissions. Emissions from these modes of transportation are included in nonroad sources. Therefore, in order to accurately characterize the contribution of transportation activities to air pollution problems in regions across the country, it is necessary to include rail, aircraft, and watercraft emissions in the analysis. However, since the estimation of emissions from nonroad sources is not the responsibility of transportation planning agencies, data required for this purpose are not discussed in this report. Motor vehicle emissions (specifically tailpipe emissions) are generally a function of the speed and grade at which the vehicle is operating, which affects the vehicle's emission rate, and the number of miles that the vehicle is driven during a specified time frame. The following equation characterizes the general relationship between these parameters and a motor vehicle's emissions: EFi x VM[j, where 1 Speed itself is a function of the type defines the specific vehicle type or class; j = defines the time frame (e.g., pounds per day); EFi; = defines the emissions factor, in grams per mile, attributable to a particular vehicle or vehicle class which is a function of average speed, grade, engine size, horsepower, etc. (estimated via an emissions factor model such as MOBILE or EMFAC); and VM[j = represents miles traveled during the specified time frame. Of roadway on which the given vehicle is operating during the specified time frame. As a result, emissions are expected to differ by type of roadway and by time- of-day. For a given calendar year, MOBILE (or EMFAC) estimates emissions factors using various transportation-related inputs characterizing travel patterns within a region or a region's motor vehicle fleet. These inputs include, but are not limited to, the following: VMT bY vehicle class: annual NCHR~ Multimodal Transportation 19 Planning Data Project 8-32(5)

Jack Falicett Associates, lilac. Final Report March l~97 mileage accumulation rates by vehicle class and model year; vehicle registration distributions by vehicle class and vintage; trip length distributions; VMT by speed class; VMT by time of day; and VMT by functional road class. Target level inventories are also required under the 1990 CAAA for ozone nonattainment areas for each three year period until attainment of the NAAQS for ozone. Target inventories are important because control strategies must then be developed so that actual emissions will meet the target levels. Target levels already account for tailpipe emissions improvements, so that mobile source emissions reductions must come from reductions in activity, specifically VMT and tripmaking. Furthermore, annual VMT forecasts (including base year estimates of actual VMT) are required for the development of emission inventories by both ozone and CO nonattainment areas (CO nonatta~nment areas classified as moderate, but with CO concentrations above 12.7 ppm). Estimates of actual VMT must be based on FHWA's Highway Performance Monitoring System (HPMS). In addition to estimates of actual VMT, forecasts of annual VMT from the base year to the year of attainment are required by the 1990 CAAA. Moderate, serious, severe, and extreme CO and ozone nonattainment areas must use network-based travel demand models for this purpose. TCM Analysts and Conformity Determil2ation -The marriage between air quality and transportation planning is most apparent in the need for and development of tr~n~n~rt~inn Unfree measures (TCMs) and in the assessment of conformity. rat r r Depending on an area's nonattainment status, TCMs are required for emissions control on the basis of VMT reduction. However, the standard transportation planning models are not sensitive to many ofthe TCMs being proposed to control VMT growth, and MPOs are evaluating the effectiveness of TCM strategies exogenously to the conventional 4-step travel modeling process. Although not identified in EPA's TCM guidance documents, data needs associated with TCM analysis are extensive and include highway system data (e.g., lane miles, lane miles of HOV, etc.), transit system data (e.g., vehicle miles, routes, riders, etc.), demand data (e.g., VMT distribution by trip length, duration of peak period, number of trips, etc.), and time or cost data (e.g., person hours of delay, parking cost, average speed, etc.). In general, data need to be developed at a more detailed spatial level for example, at the intersection level to conduct "hot-spot" emissions analysis. ~r`={^r~;t~T rl~f=~;~^f;~ ~^ +~ ~^+ ~11~:_~ ~ ~ _ ~ ^. 1 · 1 ~ , ~VlllVI1111~) -~lllItIl"~} ~-V~ tti~ til'V~t ~g ~-=l5pVrt~tlV'n pla~lng problem stemming from Federal legislation. It dictates the content of the metropolitan transportation plan in nonattainment areas, and sets the stage for interagency consultation in the development of transportation plans, TIPs, and SIPs. Transportation plans adopted after January 1, 1 995 in serious, severe, or extreme ozone nonattainment areas and in serious CO nonattainment areas must describe the transportation system envisioned for future years called horizon years. For the horizon years, the plan must formalize the relationship between travel and land use, and describe regionally significant additions to the highway and transit network in sufficient detail to allow modeling of NCHRP-Multimode] Transportation 20 Project 8-32(5) Planning Data

Jack Faucett Associates, lac. Final Report March 1997 transit ridership and travel times under various volumes. Furthermore, conformity determination must use the latest information on TOM effectiveness. Exhibit 3 depicts an example of conformity planning. The general data requirements associated with conformity for the transportation plan and TIP are listed below: estimates of current and future land use patterns, population, demographics, and employment; estimates of background levels of pollutants, transit fares, service levels, arid ridership; on-going Transportation Demand Management (TDM) or Transportation System Management (TSM) activities; emission estimates of regionally significant projects currently under construction; and TCM effectiveness estimates. In effect, conformity determination implies specific modeling requirements that go beyond the capabilities of current travel models. First, the use of a network-based model is required for conformity determination. Unlike traditional models used in the 4-step process, the network- based model used for conformity determination must account for off-net~vork travel, estimated speeds and delays in a manner that is sensitive to estimated volume of travel on each roadway segment, and provide peak and off-peak travel demand and travel times. In response, effort is underway to improve current travel models and to develop new models. Section 2.2. 1.3 describes changes to the 4-step travel modeling process that are being developed to address specific planning requirements of ISTEA arid the ~ 990 CAAA. 2.1.1.3 Planning Requirements and Strategic Data Needs Assessments The overview presented above facilitates the development of a strategic planning platform for data needs assessment. It is evident that data needs are invariably dependent-on the types of strategies that MPOs and state-DOTs will develop, implement, and evaluate to solve transportation problems and to increase system efficiency. Some strategies may evolve from planning requirements such as TCMs or the CMS, and others may evolve from the objectives that specific planning agencies strive to satisfy. Some MPOs, for instance, will not need to conduct conformity analyses, nor will they be required to develop a CMS. Consequently, one of the most pertinent questions that a planning agency must respond to is "what are the planning requirements given the region's population and NCNRP-Multimodal Transportation 21 Planning Data Project S-32 (5)

:; Exhibit 3 Conformity Planning 1 1 Determine Technical Methodology & Model Assumptions | Project I ~NO I Submissions in TIP I ~Scant Proj~, . 1 ~1 | Verify i: I areinPlan | ~ r Submit to Interagency Consultation Group Revise Methodology if Necessary Send to Appropriate Federal Agencies for Review Perform Technical Model Analysis & Produce Emissions  , Share Results with Committees & Public Prepare Final TIP & Conformity Determination Projects for "Review" & Programmatically Conforming (PC) List Highway & Transit | List of "Review" | ~List of PC Projects by Horizon Years ~ Projects l l Projects 1 ~ ~ --- 1 Quantitative & Qualitative Analysis (Status Report on TCM's) ~ , Determine Emissions 1  Source: Baltimore Metropolitan Council

Jack Faucett Associates, Inc. Final Report March 19°7 , nonattainment status, arid what data are needed to support these requirements?" A more fundamental question, however, is "what is the underlying mission of the planning agency, which plaIming objectives best support this mission, and which transportation strategies will ensure that these objectives are met?" This question serves as the analytical basis from which a comprehensive strategic assessment of data needs can be performed. As discussed in the introductory chapter, the Business Model sequentially evaluates the planning and data requirements accounting for the missions and objectives of a planning agency. The framework determines which strategies are needed to ensure that the mission and objectives of the planning agency are met, and determines the data needed to implement and evaluate the strategies. The following sub-section presents the components of the Business Model and reviews its application for assessing data needs. 2.1.2 A Framework for Strategically Assessing Data Needs 'We are entering a periods of change: a shift Mom command[-andl-contro! organizations to information based organizations of knowledge specialists ...Businesses, especially large ones, will have little choice but to become information based ... But as soon as a company takes the first tentative steps f om data to information, its decision processes, management structure and work ... begin to be transformed. '5 The evolving multimodal nature of the kar~sportation planning process, in conjunction with the efficient, safe, and environmentally compliant operation oftrarlsportation systems must be supported by a larger quantity and a higher quality of information than what has been available in the past. Fortunately, process improvements in data storage, representation, and retrieval, provide a vehicle for rapid acquisition, analysis, arid dissemination of information to meet these new data demands. Some of Me new technology advancements associated win data collection, organization, analysis, and dissemination include weigh-in motion, automatic vehicle location and identification, motorist information systems, vehicle navigational and route guidance systems, global positioning systems, remote sensing, geographic information systems, infonnation engineering and CASE development tools, and microcomputer-based data collection and analysis systems.6 There are a large number of transportation data related projects currently underway which are responding to the call for a data needs, data organization, arid data integration framework. The Travel Model Improvement Program launched a data research initiative under Track D in July, 1 995. sDrucker, P., The Coming ofthe New Organization, Harvard Business Review, Boston, MA (Jan-Fete, 1988). 6Dueker, Kenneth J., Impacts of Emerging Information Technology on Data Collection and Availability, Transportation Research Record 1253. NCHRP Multimodal Transportation 23 Planning Data Project 8-32~5)

Jack Fal4cett Associates, Ionic. Final Report March 1997 - This program focuses on improving transportation data collection, analysis, and use. Two major areas of research are being targeted: improving data collection methods to support State-DOTs and MPOs in responding to data needs for travel model improvement and development efforts. Another data related effort is the Pooled Fund Study, sponsored by FHWA and the New Mexico State Highway and Transportation Department. The numase of that ~tilelv is tr' rr-~t" ~ O`rel= _~¢ ~ ~ I, ~ ~_~- ~ O~ OL~111~ arcn~tecture and aemonstrahon prototype to address the requirements of the management systems within the context of a GIS environment. Current NCHRP research being performed in conjunction with Project 20-24(6)B is in the process of developing a business systems plan for highway engineering information using a top-down approach to account for the broad needs of highway engineering as a whole. Further, the American Association of State Highway and Transportation Of finials (AASHTO) is sponsoring research on a comprehensive transportation information planning system that uses a high-level view of agency-wide information and activities. These are but a few ofthe many projects being sponsored by various branches of government to address the dynamic and unwieldy nature of transportation information needs, flows, arid uses. 2.1.2.1 Information System Development Models Often times, when an organization defines or refines its data needs, business operations and procedures are well established. Usually, existing data are catalogued and then the Information Systems personnel assess the best methods for organizing data given the constraints ofthe processes, procedures, and structure of the organization. Most of the published literature pertaining to data needs assessment methodologies bespeaks of methods for the more efficient organization of data, rather than providing guidance on how to strategically assess data needs. Conventional methodologies used for organizing data are known as process models and have their foundation in systems analysis. Process models are developed to examine how data are used as opposed to how defined goals or objectives are reached.7 Consequently, such models stress processes over the information needed to meet objectives and support strategies. The problem with the process methods is that the resulting information systems and data do not match business needs, and the resulting data do not support organizational objectives. Process methodologies do not provide guidance on identifying the information that art orgarlization's staff and managers require for the efficient execution of work. Thus~ He massive investment in data or~nni7~tinn nrnm~t`~ ton much data and too little information. ~^~^ ~ ~1s Al VlAl~J LWO L~V Assessing an organization's data needs should be based on the stated mission of the orgaruzation as opposed to the processes that already have been developed by the organization to meet those missions. There are two reasons for avoiding process methods when assessing data needs. First, the structure of data which an organization requires is less dynamic than the structure of the 7Finklestein, Clive, Information Engineering -- Strategic Systems Development, Addison-Wesley Publishing Company, New York 1992, p. 12. NCHRP Multimodal Transportation 24 Project8-32(5) Planning Data

Jack Faucett Associ.ales, lnc Fi.~al Report March 1997 organization. That is, policies, procedures, and organizational hierarchies change more rapidly than the data and information required to fulfill organizational objectives. Consequently, assessing data needs in a manner that supports organizational objectives minimizes the emphasis on processes which, in turn, provides system stability regardless of process and organizational change. Second, process methods do not necessarily promote vertical and horizontal data integration. As depicted in Exhibit 4, process methodologies use the current system and the current organization as a baseline for assessing data needs. An assessment is made regarding how data are currently collected, stored, and distributed. Next, problems and gaps within the current system are defined. Finally, a new system is developed in order to address the organizational process related problems earlier identified. Essentially, data redundancy is highly probable because the process approach does not assess data needs respective of inters d intra-orgar~izational requirements. Fortunately, one information systems discipline, information engineering, provides a structured approach for simultaneously assessing what data are needed by an organization and determining how to best organize that information. It is the consideration of a structured assessment tool for defining an organization's data needs that is unique to the information sciences. 2.1.2.2 Information Engineering Information Engineering (IE) was conceptualized in the early 1970's and further refined during the early 1980's. The majority of the methodology constituting IE has been developed by Clive Finkelstein and his work is used extensively as a foundation for the development of the transportation data needs assessment framework. As evident in Exhibit 5, the discipline focuses on the objectives of an organization and information required to meet those objectives as opposed to an organization's current procedures. The methodology provides a framework for identifying data that are fundamental to the operation of an organization, from which information needed for decision making can be derived. Data indicate what details the operational levels of an organization need, or what details are needed to measure achievement of specific goals or objectives. It should be noted that there is extensive literature published on the conduct of an information engineering study, and the discipline, in total, has far broader applications than are of concern to the data needs of transportation planners. Exhibit 5 depicts the four-level architecture associated with Information Engineering. The shaded areas that provide the framework for assessing data needs are henceforth referred to as the Business Model. O _ ~ The Business Model-The business model is technology independent. That is, the framework for assessing data needs and understanding data organizational structures is applicable regardless of data storage collection techniques or computer automation platforms. The business model is developed NCHRP Multimodal Transportation Planning Data 25 Project 8-32(5)

Exhibit 4 Process Methodology Framework Current information System Problems & News ~ Requirements Current Organization Structure . ~ New ~r ~ Information L ~- New ~ System J ~Organization |

Exhibit 5 Information Engineering Methodology DA TA PROCESS TRADITIONAL SYSTEMS DEVELOPMENT PROCESS 1 DATABASE DESIGN _ _ ~( DATA BASE i/ ORGANIZATIONS -~ EVENTS J ~ : PROCESS MODE) - - 5_ _ ~ APPLICATION ~ ~< DESIGN J A_ 1 \ - - : APPLICATION ~ <, - c Ovals below the dotted line represent steps involved in a traditional systems development process. Shaded ovals represent major tasks comprising the strategic assessment of transportation data needs

Jack Faucett Associates, Inc. Final Report March 1997 using strategic management planning tools.8 There are six steps involved in the business model development process as represented in Exhibit 6. The first five steps of this process will be discussed here. Step six will be discussed in Section 2.2. · Step ~ identify current orgar~ization's mission. . Step 2 Define goals and objectives of organization. · Step 3 Develop strategies to meet goals arid objectives using goal analysis. · Step 4 Analyze functional responsibility. . Step 5 Develop a strategic model which identifies data entities that provide information needed by management based on the plan. · Step 6 Define the data organization structure appropriate to the strategic plan. Note that none of the steps identified above discuss the design or redesign of processes or systems. Step 1 (identifying the organization's mission) can be achieved by obtaining documentation existing within the organization. Such documentation might include a State-DOT's or MPO's transportation plan. This documentation can be further expanded by issuing a questionnaire executed to all management participants within the organization. Questions might include those related to the mission and purpose of the organization, mission and purpose of He area of responsibility, concerns and issues, organizational policies, objectives and strategies, and program priorities. These questionnaires are useful in identifying people or groups that traditionally have been omitted from the strategic planning process, in obtaining various perspectives usually constrained by management hierarchies, and in the perpetuation of vertical participation within the organization with respect to the identification of inflation and data needs. Steps 2, 3, anti 4 (defining goals and objectives, developing strategies to meet goals and objectives, and mapping these strategies to specific functions) can be achieved using goal analysis, and then defining a new strategic focus once areas for operational improvement have been identified. Strategic assessment of data needs relates to strategic management since both stress the mission of the planning organization. However, data needs assessment focuses on the information content needed and strategic management focuses on how to structure the management of the organization to achieve the goal objectives of the mission. However, strategic management is information-dependent and a synthesis, or an integrated analysis of management and information development, may be called for. See NCHRP Report 331, Strategic Planning and Management Guidelines for Transportation Agencies, December 1990. NCHRP Multimodal Transportation 28 Planning Data Project 8-32(5)

Exhibit 6 Business Mocle' Steps 1 - ,6 _ 3 ,~ 4 as 6 1 PLANNING _ PROCESS DATA MODEL

Jack Faucett Associates, luc. Final Report M`z.rch 1997 Goal analysis consists of four steps as depicted in Exhibit 7. First, goals and objectives are identified from the mission statements of the organization. Goals represent long-term targets for achievement, while objectives are more short-term in nature. For example, one MPO goal might be to improve air quality. An objective associated with this goal would be to reduce carbon monoxide (CO) type pollutants. Both goals and objectives are quantifiable and have three properties including the following: measurable attribute, which is the entity being adjusted via the goal or objective statement; . level, which indicates the quantitative value that the measurable attribute should achieve; and · time, which indicates when the level must be achieved by the measurable attribute. For example, the objective of reducing CO type pollutants would be stated as such: "Reduce CO emissions to a level below 12.7 parts per million (ppm) per eight hour time period by 1995." In this example, the measure is "CO type pollutants", the level is "12. 7ppm", and the time is "some lime in 199599. Second, it is necessary to identify any issues that might impede the achievement of the goal or objective. There are generally four or five issues identified per goal or objective. For example, issues which impede the attainment of lower CO emissions include too much congestion, high VMT growth rates, climate encumbrances, etc. Third, it is necessary to develop strategies which will effectively allow the organization to overcome the issue related impediments. Whereas goals and objectives describe what an organization wants to achieve, strategies and tactics describe how to achieve them. A strategy may comprise many steps: each of which is called a tactic. Strategies indicate how a goal should be reached within the constraints identified during issue development. Strategic statements generally start with action words such as establish, forecast, maintain, ensure, predict, etc. For example, one strategy for reaching CO attainment is the construction of HOV larches. Fourth, after issues and strategies have been identified, it is necessary to map the strategy onto an organizational function so that functional responsibility for each strategy is defined. In the above example, the strategy of constructing HOV lanes to enhance CO attainment would be the responsibility of the highway engineering division of the State-DOT. Once all strategies and functions have been identified they should be mapped into a matrix similar to that depicted in Exhibit 8. At this point in the Business Model process, the existing organizational strategy has been evaluated and it is possible to assess, via the strategy-function matrix, which organizational needs are not being NCHRP Multimodal Transportation 30 Planning Data Project 8-32~5J

Exhlblt ~ Goal Analysis Steps DeOne ~ Coals j 1 ~ identify I issues j . 1 . . ~ Define ~ . strategies Allocate ~ . strategies ~- ]

Exhibit 8 Strategy-Function Matrix .... ... . . Forecast~Trave! ~ · . . .,. ............................................................................................................................................................................... Construct HOV^ Lanes · .............................................................................................................................................................................. | Promote Public ~ l -Transportation · . - --: ,.............................................................................................................................................................................. | Improve Traffic | 1 Operations · : .

Jack Faucett Associates, inc. Final Report Starch 1997 met and where any overlap in responsibility might exist. These gaps and redundancies are the first sign of organizational inefficiencies and signal probable overlaps or gaps not only of responsibility but also in data acquisition and analysis. Next, the organization must assess internal and external strengths, weaknesses, opportunities, and threats as they may impede the execution of the identified strategies. There are a number of steps associated writhe re-definition of an organization's strategic focus. The purpose of redefining a strategic focus is to ensure that the goals and associated strategies thus identified can actually be employed. For example, though the addition of HOV larches may appear as the best strategy for reducing CO emissions, exogenous impediments may exist to the construction of these lanes. Step 5 of the Business Model, (developing a strategic model which identifies data entities Mat provide information needed by management based on the plan), begins with the redefinition of art organization's strategic focus. Redefining Me strategic focus is the first step in the development of a plan around which the strategic model is built. There are three major tasks, and marry interim tasks, associated with the redefinition of an orgaruzation's strategic focus including an internal appraisal, an external appraisal, and Me evaluation of strategic alternatives. All steps involved in defining information needs are depicted in Exhibit 9. An intemal appraisal of current operations constitutes an assessment of how well the organization is meeting its goals and objectives via some type of performance indicator. For example, a performance indicator-depicting the level of service provided by the Travel Modeling Group might consist of the accuracy of curTent forecast techniques in predicting convoluter travel patterns. Assessing how well each functional area fulfills their strategies and meets their objectives using performance indicators will provide insight into the strengths and weaknesses of those fimctional areas. Organizational strengths indicate that the functional area is currently able to meet its goals and objectives. Organizational weaknesses indicate areas which require further attention in terms of funding, information, data, or processes. In application to the transportation planning infrastructure, an external appraisal examines the environment external to the organization and identifies stakeholders. External environmental factors might be demographic, economic, social, cultural, political, legal, or technological in nature. These factors can impact transportation planning via budgeting constraints, legislative requirements, or technological opporh~niW. For govenunent type orgariizations, external appraisals should comprise the following steps: 1) Environmental Scanning, outlined by Rowe, Mason, Dickel and Snyder,9 consists of assessing economic, political, social, technological, competitive and geographic factors 9Rowe, A.J., Mason, R.O. and Dickel, K.E., Snyder, N.H., Strategic Management and Business Policy: A Methodological Approach (Third Edition)' Addison-Wesley, Reading' MA7 1990. NCHRP Multimodal Transportation 33 Planning Data Project 8-32~5)

Exhibit 9 Information Needs Steps INTERNAL APPRAISAL ASSESS INFOMRATION NEEDS ~EXTERNAL APPRAISAL - \ ~ - Environmental Scanning - Stakholder Analysis Technology Assessment - ~_ EVALUATE STRATEGIC ALTERNATIVES Strategic Gap Analysis Select Strategies - Develop Strategic Statements

Jack Faucett Associates, inc. Final Report March 1997 which might impact a given strategy. Essentially, it is necessary to determine what types of outside sources may threaten or enhance the implementation of a particular strategy. 2) Stakeholder Analysis, as outlined by Rowe, Mason, Dickel and Snyder, seeks to identify stakeholders with issues which might positively or negatively impact the success of the strategy. Such stakeholders might include sources of new technology; labor unions; suppliers; local communities; local, state and federal government; scientific labs; university researchers; public interest groups; and the media. Stakeholder analysis involves the compilation of a checklist of potential stakeholders and then asking "What are the most plausible assumptions the organization must make about each stakeholder in order for the strategy to be successful?".~° 3) Technology Assessment consisting of technology scarming and technology evaluation. Essentially, research into emerging technology is conducted and art evaluation is made as to the most likely technological alternatives to implement or pursue. Once all strategies have been identified together with the threats, opportunities, strengths, and weaknesses of those strategies, it is necessary to evaluate Me sum of strategic alternatives. A traditional strategic evaluation consists of three steps: Strategic gap analysis is used to define the strategic agenda. For government organizations, it is essential to determine what strategies are feasible to employ (as defined during the internal appraisal), but may be difficult to implement based on exogenous factors (as determined during the external appraisal). If a designated strategy has a low probability of implementation due to exogenous circumstances additional strategies must be defined. If multiple strategies are defined or required, it is necessary to make an assessment of alternatives to establish priority of implementation. Strategies are selected relative to all alternatives. One method for selecting among alternatives is the development of a strategic alternative evaluation matrix where each alternative is assessed in terms of feasibility, advantages, disadvantages, Great exposure, probability of achievement etc. Using such a matrix will provide a vehicle for easy alternative assessment. 3) Strategic statements are developed in order to define strategic direction. It is the definition of strategic statements which provides the organization with the information required to meet a given objective. Determining what types of information are required dictates the data that are needed to meet those objectives. Op.Cit. NCHRP Multimodal Transportation 35 Planning Data Project 8-32~5)

Jack Faucett Associates, Inc. Final Report Marciz 1997 _ There are a number of components involved with strategic statement development. First, a strategic rationale is developed. This rationale must include the assumptions made in the development ofthe strategy and the rationale as to why the strategy will be effective. For example, building HOV lanes to reduce CO emissions assumes that a pre-determined number of commuters will carpool. If this assumption is correct, fewer cars will be required to deliver the current number of commuters to their workplace. Fewer cars imply lower emissions. Additional components of the strategic statement include the identification (and documentation) of alternative strategies if the assumptions and reasoning behind the primary strategy are incorrect. Also, potential exogenous variables must be considered. For example, land use patterns surrounding the HOV restricted highways may impact the number of occupants per car. As land use patterns change, additional benefit may manifest if the number of occupants per car is increased. Next, the objectives associated with the strateg.v and the organization entity identified as functionally responsible for executing the strategy must be documented. Also, the measurable attribute, level and time associated with each objective must be documented. Finally, resource commitments are identified, priorities and time frames for implementing the strategy are identified, and actions necessary to implement the strategy are specified. The result of this strategic statement is a document defining exactly what needs to be done, when it needs to be done, how it should be done, and why it is being done. Note that all information requirements necessary for the execution of this strategy will be contained in this statement as well. 2.1.3 Application of the Business Model to Assess Data Needs The end goal ofthis study is to develop a comprehensive and integrated data program that minimizes redundancy in data collection and provides techniques for data storage and sharing across all user groups. The Business Model facilitates the achievement ofthis goal since it can be applied to assess the data needs of any planning agency. This section exemplifies the application of the Business Model to assess data needs associated with selected strategies that support multimodal transportation planning objectives of both the state-DOTs and MPOs. First, the mission of the planning agency is articulated. Second, a description of planning objectives is provided. Third, a detailed analysis of strategies supporting one ofthe objectives is presented, and an assessment of data needed to develop, evaluate, and implement the strategies is conducted; therefore, the Business Model is carried through for only one primary planning objective. Although ISTEA promotes collaborative planning between MPOs and state-DOTs to ensure a comprehensive and integrated planning process, state-DOT planning objectives often differ from those of MPOs. Differences largely stem from the spatial context of transportation needs and differences in state and MPO functions. MPOs focus on transportation strategies that solve local problems within the metropolitan area, often from the perspective of demand management and NCHRP Multimodal Transportation 36 Planning Data Project 8-32(5)

Jack Faucet! Associates, Inc. Final Report . Larch 1997 system efficiency. In contrast, state-DOTs must address transportation needs and problems across both rural and urban areas, and often focus on system preservation, maintenance, and expansion to meet the mobility needs of state citizens and commerce (i.e., supply oriented issues)." Exhibit 10 depicts the relationship between state-DOT and MPO planning objectives. It should be noted that the generic classification of state-DOT and MPO planning along the lines of demand versus supply oriented strategies is employed in this study solely for the purpose of facilitating the application of the Business Mode' to separately assess the data needs of state-DOTs and MPOs. Obviously, many planning strategies are developed by state-DOTs and MPOs that transcend the dichotomy of supply versus demand. This dichotomy should not be interpreted as a recommended separation of responsibilities. In fact, the intent of ISTEA is to ensure comprehensive planning and promote unprecedented cooperation between planning agencies. Application of the Business Model ensures that a comprehensive and integrated multimodal transportation planning data program is developed. 2.1.3.1 Sample MPO Data Needs This subsection assesses data needs associated with one of the primary planning ob jectives of MPOs: improving system efficient bv improving con~.~tinn m~n~em~nt ~ ~ , , , ~ =, ~ . ^_. I. Application of the Business Mode! for this purpose follows the steps outlined in Exhibit 7. f Step 1: Deft ne Mission The planning mission of MPOs can be articulated as follows: To ensure that the mobility and accessibility needs, within the metropolitan area, of both people and commerce are met, while concurr~ntiv minimizing the r]PfrimD'qt^ externalities associated with transportation. - _ _,, ~, . __. ~v- · -~ v ~ ~ ~_ w~ ~ ·~ ~ ~-~ ~L ~ Implied in this mission statement is the need to ensure that the efficiency ofthe transportation system is optimized. At the MPO level, system efficiency involves two fundamental planning issues: liFor example, ISTEA's management system regulations have provided considerable flexibility to tailor the CMS to the individual needs, capabilities, and resources of States and MPOs. Thus, transportation system performance objectives and congestion management strategies may vary between States, between the State and metropolitan areas, and between metropolitan areas within a State. Typically, MPOs focus on demand and operational management strategies such as HOV strategies, transit operational improvements, incident management, and traffic operation improvements, while State-DOTs focus on capital improvement strategies such as lane additions and transit capital improvements. NCHRP Multimodal Transportation Planning Data 37 Project 8-32(5)

Exhibit 10 Link Between State-DOT and MPO Planning Objectives _ _ Transportation System ,_ ~3

Jack Faucett Asssc~tes, Inc. Final Re.port l March 1997 the development of planning objectives and supporting strategies that strive to anticipate growth in travel demand stemming from demographic and socio-economic factors, and the development of objectives and strategies that strive to improve the management of transportation systems. Both of these issues address the need to mitigate congestion, air pollution, energy consumption, ansportation-related injures and fatalities, noise, and over detrimental externalities associated with the transport of both people arid goods. Step 2: Defile Goals and Objectives-Based on the assumption that MPOs focus planning activities around transportation demand issues, generic statements of planning objectives can be formulated. The following primary objectives have evolved as Me basis for meeting the overall mission of MPOs as articulated above. Note that the order in which primary objectives are discussed below does not imply a ranking of importance. Primary Objective No. 1-Assess the current and future demand for transportation services within the metropolitan region. An important planning strategy employed by MPOs to support this objective is travel demand forecasting. Conventional travel demand forecasting follows a behavioral paradigm that defines Gavel demar~d to be derived Tom the dally activities of individuals and businesses. Travel forecasting infers Tom the spatial distribution of activities the amount, type, and location of travel that a population will undertake. Regional travel forecasting requires: 1) gathering a large number of data inputs at the lowest practical level of aggregation; 2) obtaining plausible forecasts of data inputs such as population, income, and fuel price; 3) developing models to accurately represent travel behavior; arid 4) applying these models to the forecasted data inputs to produce useful forecasts of fixture travel patterns. 12 Exhibit 1 1 depicts the components of conventional regional travel forecast models. ISTEA and the 1 990 CAAA have imposed significant analytic hurdles that conventional models cannot overcome. As a result, MPOs have responded by altering models to address planning requirements associated with these two federal initiatives. Changes and data needed to support new modeling frameworks are discussed later in this Chapter under Section 2.2. 1.3. Primary Objective No. 2-Minimize the contribution of transportation to regional air pollution. MPOs are responsible for developing arid implementing emission mitigation strategies that focus on reducing VMT and ~ipmalcing. Such strategies include measures that promote shifts from single occupancy vehicles to high occupancy vehicles, improvements in traffic management tiDeakin Harvey Skabardonis, A Manual of Regional Transportation Modeling Practicefor Air Quality Analysts for the National Association of Regional Councils, July 1993. NCHRP Multimodal Transportation 39 Planning Data Proyect 8-32¢5)

Exhibit ~ ~ Components of Conventional 4-Step Moclels Regional Economics & Regional Demographics Sub-Regional Distributions: Population by Income Level Economic Activity Auto Ownership Number of Trips _ ~- ~ ~ ~Equlibration _ 1 ' ~ 1' t Trip Disl tribution <_ Mode Choice 1, ~ Peaking Characteristics Traffic Assignment to Networks 1 ~1  Calculation of Impacts Source: Deakin Harvey Skabardonis, A Manual of Regional Transportation Modeling Practice for Air Quality Analysis, for the National Association of Regional Councils, July 1 993.

Jack Faucett Associates, Inc. Final Report llIarch 1997 - that increase average speeds along corridors and network links, and capacity expansion that decreases congestion along specific roadways. This objective is supported by federal mandates requiring the implementation of TCMs in metropolitan areas experiencing serious or severe air pollution problems and requiring conformity between transportation and air quality plans. However, motor vehicle emissions mitigation strategies that focus on directly decreasing the emission rates of vehicles, such as inspection/maintenance programs and/or reformulated fuels, are not the responsibility of MPOs. Those strategies are developed and implemented by state departments of environmental quality. Primaly Objective No. 3-Identify, develop, evaluate, and implement regional transportation projects that address the travel needs, both current and future, of people and commerce. This objective involves the development of Tips arid strategies for the allocation of resources among transportation projects. Primary Objective No. 4-Improve the efficiency of the transportation system by better managing the use of the system. The purpose of congestion management planning is to encourage more efficient use of existing transportation facilities. Greater efficiency is achievable by reducing the number of vehicles on transportation facilities, especially during peak demand; increasing the number of people transported per transportation vehicle; and optimizing the operational efficiency of transportation systems. The need for a congestion management plan arises from local legislation that either implicitly or explicitly requires its preparation by MPos.~3 Congestion management, therefore, does not only address efficiency improvements of current systems, but also addresses the system's ability to meet the future travel needs of people arid goods as metropolitan areas grow. To meet this objective, MPOs have developed, evaluated, and implemented various strategies. For example, transportation demand management measures, traffic operational improvements, public transit capital improvements, and measures to encourage Me use of non-traditional modes comprise combinations of strategies that support the development and implementation of a CMS. Likewise, transportation control measures (TCMs) are defined as strategies that reduce the number of vehicle trips, the number of vehicle miles of travel (VMT), arid/or increase the efficiency of travel. Consequently, strategies for meeting this objective parallel those for meeting air quality objectives. The selection of strategies for implementation, however, must consider several important objective- specific characteristics. These characteristics include timing issues and measurable attributes and levels that must be considered to ensure that selected strategies support the plarming objectives i3Baltimore Metropolitan Council, Developing a Congestion Management Plar' for the Baltimore Region, Draft, September 15, 1994. i4National Highway Institute, Three Day Training Course: Congestion Managementfor Technical Staff, Participantts Workbook, NHI Course No. 15259. April 1994. NCHRP-Multimodal Transportation 41 Planning Data -Project 8-32(5)

Jack Faucett Associates. Inc. Final Report . . Adz 1997 Time-The implementation time-frame reflects ease of strategy implementation. For instance, some strategies, such as congestion pricing and ITS, will be difficult to implement in the near term because of institutional, political, and technological barriers. In contrast, the implementation of strategies such as HOV lanes and ridesharing is only constrained by normal delays associated with project evaluation' funding' etc. Measurable Attributes and Levels-1) Geographic application indicates the spatial extent of potential strategy impact. In general, the application of congestion mitigation strategies can constitute area-wide, sub-area, corridor, and/or localized (i.e., spot) impacts. For example, HOV lanes involve corridor-specific effects, while improving transit service quality may mitigate congestion area-wide. 2) Project cost (capital arid operating costs) involve up- front, one time costs and recurring (e.g., annual) costs borne by the public sector. 3) Impacts measurements involve the direct and indirect benefits ofthe proposed strategy. For example, the benefits of strategies for improving system efficiency often are measured in terms of delay reductions (e.g., reductions in vehicle hours of travel, or VHT). Indirect strategy impacts, such as air quality, social, and safety impacts in the case of improving system efficiency, highlight the inter-relationship between planning objectives, since often more than one objective may be addressed by a single strategy. This is true of most TCMs required by the 1 990 CAAA. 4) Cost-effectiveness relates the cost of a strategy relative to its expected benefits. Cost-effectiveness can be measured in terms of cost/VHT reduction, cost emissions reduction, etc. The characteristics described above indicate the need for systematic evaluation criteria to ensure that the relevant planning objective is satisfied in the most efficient and equitable manner. Trade-off analyses must be performed to ensure this goal. The remainder of this sub-section applies the Business Model to this last objective-specifically, to MPO congestion management strategies for improving system efficiency. Congestion management and TOM strategies supporting this objective are reviewed in detail. Data needs associated with the development, evaluation, and implementation of strategies are presented. Step 3: Develop Strategies to Meet Goals and Objectives-There are three basic categories of congestion management strategies: demand management, operational management, and capital intensive improvements. Exhibit 12 lists specific strategies for each of these basic categories. The strategies listed in this exhibit typify the types of actions that should be considered in the development of a CMS. However, various other legislative objectives may address congestion management, and there is a need for coordinated planning to meet the objective of improving transportation system efficiency. First, the 1990 CAAA specify the following TCMs for consideration in the development of the transportation portion of State Implementation Plans for severe and extreme ozone nonattainment areas and serious carbon monoxide nonattainment areas: programs for improved public transit, HOV NCHRP Multimodal Transportation 42 Planning Data Project 8-32(5)

Exhibit 12 Cement Strategies Category 1 -Strategy Classic Demand Management Operational Management Capital Intensive Improvements Transportation Demand Management Measures Measures to Encourage HOV Use  Public Transit Operational Improvements Measures to Encourge Non-Motorized Modes Congestion Pricing Growth Management Traffic Operational Improvements Access Management Techniques Incident Management Techniques Intelligent Transportation Systems Addition of General Purpose Lanes Public Transit Capital Improvements l strategies · · ~ ... Carpooling Vanpooling Alternate work hours Telecommuting Parking management HOV lanes HOV ramp bypass lanes Guaranteed ride home program Employer trip reduction program Service enhancement or expansion Traffic signal preemption Fare reductions Transit information systems Bicycle facilities Pedestrian facilities Ferry service Congestion pricing Land Use Policies/Regulations Design Standards Location of jobs and housing  Intersection and roadway widening Channelization Traffic surveilance and control systems Motorist information systems Ramp metering Traffic control centers Computerized signal systems Driveway control Median control Detection Response Clearance Information/routing Advanced Traffic Management Systems Advanced Traveler Information Systems Advanced Public Transportation Systems Advanced Vehicle Control Systems Freeway lanes Arterial lanes Exclusive rights-of-way Bus bypass ramps Park-n-ride and mode change facilities Paratransit services

Jack Faucet! Associates, inc. Final Revolt Alarch 1997 lanes, employer-based transportation management, trip reduction ordinances/programs, traffic flow improvements, parking facilities for HOVs and transit service, programs to restrict vehicle use, shared ride services, non-motorized vehicles or pedestrian rights of way, bike lanes/secure facilities, programs to control extended idling of vehicles, reduction of cold start emissions, employer sponsored flexible work schedules, programs to facilitate non-auto travel, construction of non- motorized/pedestrian paths, and removal from use and marketplace of pre-1980 passenger cars and light trucks. Some of these TCMs directly address motor vehicle emissions, such as programs to - remove from use older, higher emitting vehicles, while others address system efficiency directly and overlap with strategies that support the CMS. Exhibit 13 depicts the link between congestion management strategies for CMS implementation and TCMs for air quality planning. Second, there are direct congestion management links between the CMS, IMS, and PTMS, and the development, establishment, and implementation of these management systems should be coordinated. The IMS is similar to the CMS in that it will collect data and evaluate the performance of elements of the transportation system. The elements of the transportation system to be evaluated by the IMS focus on intermodal facilities, including, for example, highway components providing terminal access to park-and-ride facilities. The links of the IMS to the CMS are summarized as follows: · effect of congestion on modal transfer, . effect of transfer facilities on system operation arid congestion, · coordination in the development of improvement recommendations, and . data and information needs for strategy evaluation. Third, although the PTMS is an asset-based management system, as compared to the CMS which is a performance-based system, information from the PTMS can be employed to determine how the transit system can be used to mitigate congestion problems given the existing or anticipated inventory of equipment, facilities, or rolling stock. Similarly, information from the PTMS can be used to assess the necessary increase in public transit facilities, equipment, or rolling stock associated with congestion management strategies that focus on increasing transit ridership. Exhibit 12 end the list ofTCMsincludedin the l990CAAA, fisted previously, exemplify the many strategies available to MPOs for mitigating congestion and improving system efficiency. To ensure that chosen strategies best meet the planning objective, trade-off analyses must be performed that address the objective characteristics previously described. Step 4: Map Strategies to Functions-The mapping of specific strategies to planning functions within an MPO obviously depends on many organizational factors that differ across MPOs. For instance, MPOs that are in nonattainment of air quality standards may have divisions that focus on NCHRP-Multimodal Transportation 44 Planning Data Project 8-32~5)

Exhibit 13 Link Between TCMs ant! Congestion Management Strategies SIP Process SIP TCMs - - - - - - 1 ' 1 TOM Impacts Source: NHI Course No. 15259 . , Consistency Evaluation Planning Process & TIP Development CMS System Performance Analysis Estimated Congestion Levels - - - - -  - - 1 Congestion Management Strategies . _ _Revise as Needed_ _ _ _ _ ~ _,

Jack Faucett Associates, Inc. Final Report Marc,\ 1997 air quality planning and TCM development, evaluation, and implementation. Likewise, depending on the size of the region, transit-related strategies may be the responsibility of the region's transit authority, while traffic operation improvements may fall under the jurisdiction of traffic centers. Clearly, given the wide range of congestion management strategies, maIly different offices and divisions of regional planning organizations will be involved, and collaboration is imperative to ensure that congestion management strategies support planning objectives. Step 5: Assess Information Needs-The strategy characteristics described above help to identify the types of data necessary for strategy development, evaluation, and implementation. Three types of data are required: 1) supply data to assess the location arid severity of congestion and the types of strategies needed for mitigation; 2) demand data to determine the potential impacts of strategies, 3) performance data to assess Me system's performance and strategy impacts. Supply, demand, and performance data need to be developed for each location of interest given specific analytic objectives and the geographic application of a particular strategy. For example, to evaluate and implement ramp metering, data on system (supply), demand, and time/cost (performance) of Gavel are required at the corridor level. Likewise, data to assess the impacts of TCMs on air quality may be required at the grid square, intersection, or transit route level depending on the strategies being considered for implementation by an MPO. Exhibit 14 presents specific data items under each of these data components. Step 6: Deft ne Data and Develop Processes-Having identified the data needs associated with congestion management strategies, the next step of the Business Model addresses the types of analyses and analytic tools necessary for strategy development, evaluation, and implementation. Many types of congestion management analysis activities must be undertaken by MPOs. These include, but are not limited to, the following: identify existing arid fixture congestion on points along the system; identify congestion problems and causes; evaluate strategies to- solve congestion problems occurring within a region, corridor, or subarea; predict system performance given a set of strategies; arid evaluate strategy effectiveness.'5 To perform these analytical activities, MPOs must rely on analytical processes and tools such as travel models arid collection of empirical data. There are a variety of analytical tools that have been developed by transportation planners to analyze the absolute arid relative benefits of transportation strategies. Although many tools are not optimal for assessing both direct and indirect impacts of congestion management strategies, analysis activities related to congestion management can be conducted using tools readily available to most practitioners. However, practitioners recognize the need to improve analytical techniques to ensure that accurate and reliable estimates of strategy impacts are developed. Exhibit 1 5 exemplifies the types of analytic tools available to practitioners to evaluate and implement various congestion management strategies. i5Deakin Harvey Skabardonis, A Manual of Regional Transportation Modeling Practice for Air Onalih' Analysis' for the National Association of Regional Councils' July 1993. a ~ ~J ~ ' ' - NCHRP-MultimodalTransportatiorz 46 Project8-32(5) Planning Data

Exhibit 14 Data Items for Congestion Management Strategies . ......... .. , ~- .. j= ~jj lI~-?~-~i=s'~}~-..~!~¢i>~-,~91¢,<~ ~1 Highway System: ~ Number of vehicles using HOV Person hours of delay Lane miles I lanes Vehicle hours of delay Lane miles of HOV | Number of persons using HOV | Average Speed Capacity | lanes | Peak period speed Functional Class I Duration of peak period I Average travel time: Portion of system congested | VMT distribution by trip length | Peak and off peek ll Nature & location of | % VMT by operating mode | % of travel congested/delayed || construction | % VMT by vehicle class | Parking cost ll Location/duration of incidents | Number of trips: starts & parks | Running speeds by hour of day, || | Park duration | rea type and facility type Transit System: I VMT by hour I Travel time Vehicle hours | Number of vehicles by class | Travel time by trip purpose Vehicle miles | Age distribution of vehicle fleet I Trip length by trip purpose Routes | Trips by vehicle class | Trip cost by trip purpose Riders | Increase in trips of one purpose | Garages | as a result of a TOM l Park & ride lots | Amount of vehicle idling time I . Transfer stations | Bus ridership l | Rail eldership l Other: l Truck freight facilities | Trips: . l Employment sites by size | VMT, DVMT l PMT | Congested or Delayed: l Percent of travel Percent of travellers | Percent of vehicles l

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Jack FaucettAssociates5 Inc. Final Report March 1997 2.1.3.2 Sample State-DOT Data Needs The introduction to this task defined multimodal transportation planning to include activities undertaken by MPOs and state-DOTs that ensure equilibrium between transportation demand and supply. Literature review and research regarding state-DOTs' transportation planning, as well as an examination of recent draft statewide transportation plans developed by New York (7/95), Wisconsin (3/95), Vermont (3/95), and Oregon (1 1/92),~6 seem to indicate that state-DOTs are primarily, but not exclusively, responsible for planning strategies that address system supply (e.g., system maintenance and the execution of expansion projects). This, in conjunction with a state-DOTs larger area of operations and management, warrants the broad-based and generic planning objectives identified in this sub-section. The objectives and strategies as set forth are meant to fit the entire spectrum of statewide transportation planning. Obviously, most state officials will need to select those strategies and data that pertain to the specific planning issues they are confronting or will likely be confronting within the planning year horizon. The Business Model is applied in this sub-section to demonstrate how a state-DOT's planning framework (e.g., mission, objectives, strategies) can be clearly outlined and, in turn, used to identify the data needs required to satisfy statewide multimodal objectives. Step 1: Deft rue Mission The planning mission of state-DOTs can be broadly stated as: To ensure that the needs for safe, efficient, and cost-e~ective travel, as demanded by citizens and the commerce community within the state, are satisfied. Among the various other sub-elements of this overall mission, state-DOTs must set long and short range plans and programs to ensure effective implementation of this mission. Each state-DOT has a planning arm to carry out this mission within guidelines established by state legislatures and/or state transportation commissions. Those legislative guidelines require that the mission and subsequent statewide policies, plans, and improvement programs must be consistent with the following: the State's other economic, social, and environmental policies; the plans and programs of local governments, as well as the officially constituted regional and metropolitan planning organizations; i6It is recognized that some of these plans are in draft form awaiting further review by officials and the public. Nevertheless, the plans are viewed as the most recent documentation of comprehensive multimodal transportation planning. This belief is supported by the fact that all four transportation plans cited have been accepted by FHWA as meeting statewide planning requirements. NCHRP Multimodal Transportation 49 Planning Data Project 8-32~5)

track Faucett Associates, Inc. Final Report the plans and programs of private providers of transportation; and · national policies. Marcy' 1997 - In addition, state-DOTs must provide both guidance and transportation data and other information to regional and metropolitan planning organizations and local governments in order to assist them in their development of transportation plans, policies, and improvement programs that are consistent with statewide objectives. Furthermore, state-DOTs must develop and otherwise acquire information required by the federal government to assist in the formulation of interstate, national, and international transportation policies. Step 2: Deft ne Goals and Objectives-Within the scope ofthe above described state-DOT planning mission, six primary planning objectives can be articulated as follows: assess the current and future travel needs of citizens; provide the physical needs of the transportation infrastructure, including interfaces or linkages between modes; satisfy mobility needs; establish resources needed to fund transportation plans and programs; integrate infrastructure (physical) and mobility needs with long-range funding scenarios; and establish improvement programs. Although all the functions of statewide multimodal transportation planning fall within one or more of the six primary objectives, it is important to note the bias towards transportation supply information represented in the objectives. The first objective, although focused on travel demand information, supports the supply-side decision making that will ultimately need to occur under subsequent objectives. Whereas demand information collected at the MPO level is more geared towards things such as managing congestion or other demand oriented objectives, state-DOTs analyze demand data to assess the ability of transportation systems (e.g.7 roads bridges' intermodal facilities' etc.) to meet current and anticipated demand. A more complete description of the objectives and corresponding strategies is presented below for each primary objective. Following the description of the remaining objectives, the second primary objective listed above will be used to illustrate the application of the Business Model. Details will be given as to the specific strategies and data needs that should be addressed in order to successfully meet this objective. NCHRP Multimodal Transportation 50 Planning Data Project 8-32~5)

Jack Faucett Associates' Inc. Final Report March 1997 Primary Objective No. 1-Assess the Current and Future Travel Needs of Citizens. State- DOTs must be able to gauge the transportation supply anticipated by its current and forecasted residents in the near and distant (20 years) future. Understanding the travel needs (demand) of its citizens is the primary information needed to attain such a goal. Each of the four statewide plans reviewed established basic parameters focused on this objective. To make the overall objective more workable, it is divided into three sub-objectives by further defining needs of"citizens": 1) needs of individuals end their families; 2) needs ofthe community at larch once 1N natal ~fhil~inmee Or government' and the military. - -A ~ - -~ of ~ 7 ~- -J '^-~ - ~ ~ V ~ - jet I' ill" "~ ~ y 3 Strategies necessary to meet this objective could include: accumulate data on demographic and behavioral characteristics (individuals, families, businesses); compile lifestyle, personal safety, and employment factors, identify transportation's impact on the environment; analyze congestion management information; identify current uses by industry and military, etc. Such data as state Population. emnlovment. economy Dine vehi~.l~ nor hnilc~h~lr1 ~ He;: ~ _ ¢ ~ _7 ____~__J ~ ~ ~ .~ 3 ~3 ~ _~O ~1 lIV~11~1~ t11p ~11~1~10~ 1 ~ ~ · . . , . _ . ana use, freight te.g.' tonnage' commodities' intermodal facilities characteristics), and environmental facts need to be assembled and forecasts made to gain the overall dimensions oftravel demand. A majority ofthe data needs identified will also be essential for planners confronting other objectives at the state level and for MPO transportation planners. In fact, much ofthe data may have already been collected at the MPO level for the purpose of travel demand forecasting and may be aggregated by state planners for their use. In this manner, coordination among agencies would reduce redundancy in data, as well as provide added consistency and standardization. Primary Objective No. 2-Provide the physical needs of the transportation infrastructure, including interfaces or linkages between modes. The statewide plans reviewed in this study all focus on physical needs, including the linkages or lack of, within and between systems and modes. In fact, the first priority in each statewide plan is directed toward maintaining the existing physical plant, the elimination of the backlog of previously identified physical needs (maintenance and/or expansion), and the avoidance of further physical degradation of the system(s). The principal strategies to meet the physical needs of the transportation infrastructure mav include nerfr~rmin~ either maintenance or undertaking system expansion projects, or both. ~, ~ A ~ o Primary Objective No. 3-Satisfy Mobility Needs. It is acknowledged that there is significant overlap between this objective and the one discussed immediately above. However, highlighting the multimodal aspect of state-DOTs activities has become a practical exercise in light of recent legislation. Three major sub-objectives further focus planning activities associated with meeting mobility needs: satisfy intercity mobility needs, · satisfy metropolitan areas' mobility needs; and O NCHRP-Multimodal Transportation 51 Project S-32~5) Planning Data

Jack Fa1lcett Associates, Inc. Final Report March 1997 satisfy rural areas' mobility needs. Each of these sub-objectives has personal and goods movement elements and modal and intermodal transfer elements. Sample strategies associated with meeting this primary objective include conducting Ravel demand analysis (e.g., intercity personal and goods movement, metropolitan transit use, intetmodal transfers, rural personal and goods movement, etc), assessing the current capacity of various intermodal services, and evaluating the need for system expansion. The magnitude of data needed to adequately address this objective is extensive. A small sample of what would be needed is: person and freight trips (truck, rail, air, port and waterway, pipeline), trip lengths' person miles, travel time, travel speed, frequency of service, deficiencies, capacities, costs of operation and capital, etc. Performance measures, deficiency identification, cost to meet deficiencies, and cost-effectiveness are kev data needle for n11 ~tr~teni-~ that foully ^= ^^rc'^~1 ^~1 goods mobility. J ~^ ~_~- ~ ^~ V11 ~lOVll~1 ~1~ The four statewide plans reviewed in this study address the need to dampen the expected increased demand for highway use, even in those states where population and economic increases are projected to be small by national standards or in relationship to previous decades' growth. Data that focus on diversion of highway trips to intercity rail, urban transit, and waterways are. therefore .snecifi~1 n a type of information needed to meet mobility needs. ~ · ~ · , e ~, ~ ~ , ~ .- ~ Primary oo~ect~ve No. 4-Establish resources needed to fund transportation plans and programs. The fourth major statewide multimodal transportation planning objective involves the review and establishment of financial resources to fund the alternative and final plans. This objective is considered an essential statewide planning objective. All four statewide nl~n~ Arming financial implications in some detail. ,_ _, ~,~r~~-~~~- Strategies to successfully meet this objective include identifying all potential sources of funding (federal, state, local, and private), including indirect sources such as tax breaks and subsidies, as well as estimating the costs involved with particular plans and programs. Funding strategies could include raising toll charges or transit fares, applying for federal fur ds, issuing bonds, etc. ~ A r ~^ ~ ~_ Associated data needs involve collecting totals on tax income (e.g., fuel tax), transit fares, bridge and highway toll totals, airport landing fees, etc. Locating funding sources entails collecting data on, for example, federal aid programs, bond potential, and state and local budgets. Primary Objective No. 5 Integrate infrastructure (physical) and mobility needs with long- range funding scenarios. This objective does not involve data, but is the planning that culminates in the development ofthe long-range statewide plarl. It includes combining those improvements that NCHRP-Multimodal Transportation 52 Project8-32(5) Planning Data

Jack Faucett Associates, Inc" Final Report March 1997 are found to be cost-effective, that fall within the funding constraints, and that meet the overall transportation needs of citizens. Primaly Objective No. 6-Establish improvement programs. Statewide transportation planning involves the establishment of a long-range transportation vision from which short-range improvement programs (three to ten years) can be fashioned. This primary planning objective links the long-range plan with project implementation. From the many improvement project possibilities included in the long-range plan, a finite number must be selected to meet the funding resources that are likely to be available over the program period. If not already made by the legislature, decisions have to be made on the apportionment of funds among regions and between rural and urban areas. Decisions also must be made on integrating regional, metropolitan, and statewide priorities and projects. Consideration must be given to many issues, including the following: · legislative authority, when needed, for some actions; . . . lead time required for environmental assessments, preconstruction of way clearance, if necessary; status of projects "in the pipeline"; staging of improvements; and funding constraints (categorical and otherwise). engineering, and right The remainder of this sub-section illustrates the application of the Business Model to the second primary objective: provide the physical needs of the transportation infrastructure, including linkages or interfaces between modes. Step 3: Develop Strategies to Meet Goals and Objectives-The remaining steps in the Business Model application are described as an example of how it applies to one of the primary objectives identified above (Primary Objective No. 2). Two broad strategies support the objective of meeting the physical needs of the transportation system. First, state-DOTs implement strategies to ensure system preservation given the anticipated use of facilities. Maintenance includes the restoration, reconstruction, and replacement of links whose conditions are deemed as inadequate in meeting previously established performance measures. Research indicates that as much as three-fourths of all transportation system investment (i.e., the construction or improvement budget) is directed toward maintaining physical integrity. Second, state-DOTs implement strategies to expand the transportation system, in terms of facilities and/or services. Examples include the construction of new facilities (e.g., highways) to meet growing demand, capacity expansion, and transit service expansion. NCHRP Multimodal Transportation 53 Planning Data Project 8-32~5)

Jack Faucett Associates, ~c. Final Report March 1997 To determine which of the two strategies is necessary, where to perform work, and on which mode, several other subordinate strategies need to be addressed which can assess the current and future state ofthe transportation system. For example, utilizing the sufficiency rating systems for both highways and bridges are strategies for the periodic assessment of overall trends (i.e., whether conditions and services are getting better, worse, or remaining static), for establishment of performance measures, and for the determination of specific deficiencies and their improvement. Currently, highway and bridge sufficiency ratings systems employed by state-DOT planning divisions are the most common tools for evaluating state highway and bridge conditions. Step 4: Map Strategies to Functions-The issues associated with the mapping of strategies to organizational functions (Step 4 of the Business Model) are similar to those addressed in Section 2.1.3.1. regarding MPO data needs. For example, depending on the size of the region and sophistication of transportation systems, transit-related strategies may be handled by local transit authorities rather than any state division. Furthermore, differences among states, from their organi^zational structures to geography, population, and infrastructure needs, guarantee differences in approaches and data needs, altering the mapping of strategies to functions. Coordination with regional organizations will, however, be vital in order to meet the demands of this planning objective due to the large amount of travel demand data MPOs collect which are essential for state-DOTs assessment of the future physical needs of the statewide transportation system. Step 5: Assess Information Needs-Exhibit 16 provides the summary of the data needs corresponding to the strategies that support this planning objective (i.e., perform maintenance or expand capacity) on individual transportation infrastructures (i.e., state highways and bridges, bicycle~urban transit intercitv bus intercilvrnil rllrrtlh~/n~r~tr~n`2it ~'rm~rt~ p^~,lu~l~,l-,` 1< Strategies and Data Requirements for Objective No. 2: Provide the Physical Needs of the Transportation Infrastructure' including Intennodal Links waterways' pipelines' and local roads and bridges). 7 ~7 ~B_~ ~ ~4 A. ~$ AJAR ~ ~.~ _ ~ ~ ~ ~ ~ .- ~ ~ ~ ~ ~ '. A ~ ~ ~ ~ ~ ~ ~ ~ ~ Step 6: Deft ne Data and Develop Processes-If implemented, the bridge, pavement, intermodal facilities and systems, and public transportation management systems will be instrumental in meeting the objective of providing the physical needs ofthe transportation infrastructure. The management systems that could be developed and/or implemented would be important tools for determining not only deficiencies, but also alternative corrective actions, the cost-effectiveness of the candidate improvement projects, and the priority or urgency ofthe need. The BMS, PMS, IMS, and PTMS, therefore, can be construed as analytical processes designed to support the planning objectives identified above, and, if implemented, could provide much of the data listed in Exhibit ~ 6. The management systems provide a means for structuring a transportation plan's fixture system and performance standards, and provide monitoring toward their achievement. The monitoring will, in turn, indicate which transportation investments are most effective in improving system performance. Analytical requirements associated with the establishment of asset-based management systems will draw on conventional engineering tools and processes. NCHRP Multimodal Transportation 54 Project8-32~5) Planning Data

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Jack Faucett Associates, Inc. final Report Marciz 1997 The analytic requirements associated with the EMS include: condition analysis' which includes ride, distress, rutting and surface friction assessments; performance analysis, which includes pavement performance analysis and an estimate of the remaining service life; investment analysis; engineering analysis; and annual evaluation and updating as necessary of the PMS based on the agency's current policies, engineering criteria, practices, and experience. The analytic requirements associated with the establishment of a BMS include: predict the deterioration of bridge elements with and without intervening actions; identify feasible actions to improve bridge conditions, safety, and serviceability; estimate the cost of actions; estimate expected user cost savings for safety and serviceability improvements; determine least-cost maintenance, repair, and rehabilitation strategies for bridge elements using life cycle cost analysis or a comparable procedure; perform multi-period optimization; use feedback from acorns taken to update prediction and cost models; and generate summaries and reports as needed related to planning and programming process. The analytic requirements associated with the IMS include: the identification of intermodal facilities; the identification of performance measures to measure the efficiency of the facilities and systems in moving people and goods, and system monitoring. Finally, the analytic requirements associated with the implementation of a PTMS include: the identification of costs, funding sources, and priorities of proposed projects (maintenance, replacement, or expansion); and the identification of data on the condition, capacity, and cost of public transportation facilities, equipment, arid rolling stock. Data needs associated with each of these elective management systems are summarized above in Section 2. ~ . ~ . 2.2 Task 2: Data Organization Frameworks As discussed in Section 2. I, the myriad of data needed for transportation planning evolves from the multiple objectives inherent in this planning, the strategies employed to meet these objectives, and the information needed to implement these strategies and to analyze the tradeoffs among conflicting objectives and strategies. The broad objective of transportation planning is to provide spatial access of people and goods to other locations consistent with the following constraints: economic efficiency and resource affordability; safety, health, and welfare of the population; preservation of desirable physical and cultural environments; and NCHRP Multimodal Transportation 57 Planning Data Project 8-32~5)

Jack Facets Associates' inc. Final Report . sequential logical actions to meet the overall objective over time (logical steps or "building blocks"~. March 1997 Whether these constraints are labeled as such, or as other objectives, is immaterial-since transportation planning requires an analytical apparatus to weigh the tradeoffs among these multiple objectives. The consideration ofthese tradeoffs in transportation planning is not new, but were made explicit and mandatory in the recent legislation embodied in ISTEA and the 1990 CAAA (the mandatory requirements for the six management systems have since been suspended). Planners are beginning to quantify these tradeoffs, but only public consensus can provide weights to each of the multiple objectives in any attempt to Optimize" the outcome of transportation planning. Strategies are approaches that are devised to meet one or more of the multiple objectives, and must be analyzed for both their efficiency and the tradeoffs among the objectives. Transportation strategies give rise to information needs. But, information needs must be distinguished from data needs. Information is defined as knowledge or intelligence. It may be provided in raw data, but more often is the result of calculating and analyzing the relationships among data items. For example, data on household income may not be enlightening per se, but the relationship between income and travel characteristics is useful. Thus, data needs are derived from information needs that may be identified at the strategy level of planning rather than at the analysis (or modeling) level. This is a departure from existing practices wherein data needs and organizational frameworks are usually identified and implemented around the analytical processes. This theory is adapted from the work of Finkelstein and others who have developed concepts of information engineering for application to business organizations for business planning or business modeling.'7 There are several potential advantages in identifying data needs in association with strategies rather than with analytical processes. First, it reduces redundancy in data collection. Second, it leads to more standardization in definitions and coverage of collected data, which facilitates multiple usage. Third, it yields economies in documentation. Fourth, it identifies attributes of the subject for which data is collected that may be useful in other analyses not apparent to the narrow objectives of a specific analysis or modeler (economies in data collection). Once data needs have been assessed, however, the question of data organization, embodied by data collection, storage, and dissemination issues, must be resolved. The primary objective of Task 2 is to describe current data organization frameworks employed by MPOs and state-DOTs for transportation planning purposes, and to suggest new frameworks that fully reflect the planning and data requirements associated with collaborative, comprehensive multimodal planning. Some of the major conclusions of analyses conducted under Task 2 are described below. t7Finkelstein, Clive, Information Engineering -- Strategic Systems Development, Addison-Wesley Publishing Company, New York' 1992' p.l2. NCHRP Multimodal Transportation 58 Planning Data Project 8-32~5)

Jack Fallce~t Associates, Inc. Final Report March 1997 Viewing an agency such as a state-DOT or MPO as a single entity with a unified perspective and mission often is erroneous. There may be multiple missions, goals, and implementation etr~t~rr;=c~ ~r;+h;~ the ^~_~ ~_ ~ ~ ELM 1 __ ~ , ~ o~1~1~o Wlt~lllll Ally ~lll~ ale ll~ lOl1 [llLIL produce disparate data systems and data organization procedures. Past and current data organization is by mode and along the lines of specific analysis needs of agencies that collect and maintain transportation-related information. For instance, if a data item is not pertinent from the perspective of a particular agency, either it is not collected, it is poorly maintained, or it is not organized in a manner that ensures its usefulness to other planning agencies. . Insufficient coordination has occurred in the past between the different planning and modal operating agencies with respect to data needs and organization. · To ensure a collaborative, systematic, and comprehensive transportation planning process, a new vision for data needs assessment, data organization, data collection, and data dissemination is necessary. The Business Model, for example, provides a framework for a strategic assessment of data needs. This section defines and evaluates current data organization frameworks and proposes a Data Program that transcends data needs assessment and data organization issues. Section 2.2.1 evaluates current data organization frameworks employed in existing planning processes and discusses activities on the part of transportation planners related to meeting the planning and policy requirements of Federal legislation. Section 2.2.2 proposes a Data Program that ensures coordination, organization, consistency, and integration in the planning process, and specifies the data organization framework component ofthe proposed program. Section 2.2.3 summarizes and evaluates existing traditional arid non-~aditional sources of transportation data within the nrann~1 data organization framework of the Data Program. 2.2.1 Analysis of Current Data Organization Frameworks a- -a ~ ~~ ~ To determine current data organization frameworks employed by transportation planning agencies, a number of interviews were carried out with state-DOTs and an MPO survey was executed to assess current state-DOT and MPO data uses, data collection and storage systems, and data organization frameworks. On-site visits or telephone interviews were conducted with the following state-DOTs: Maryland, New York, Wisconsin, Vermont, and Oregon. The MPO survey sample included 25 MPOs across the country varying in size. Sixteen ofthe MPOs responded, and in most cases follow- up phone interviews were performed to clarify and expand specific responses to specific questions. A listing of the MPOs that responded to the survey is provided in Exhibit 1 7. A full description of the MPO survey and a review of responses and conclusions are provided in Appendix 1. NCHRP Multimodal Transportation 59 Project 8-32(5) Planning Data

Exhibit 17 List of MPO Survey Respondents ~ a ~ .. · . ·~i ; j.' . . . .. A ~ .. .... - sunder 25,`000 :: . . . ~ 25~0,¢0~0004L0001- ~ ~: i -a; . .. . .. .. .. .. ~ ..~ i. .... ; , ; . . ~;ooo.~()~ ;. Pueblo, Colorado Worcester, Massachusetts Grand Folks, North Dakota Jackson, Mississippi Boise, Idaho Daytona Beach, Florida Tucson, Arizona 4 Harrisburg, Virginia Oklahoma City, Oklahoma Rochester, New York Portland, Oregon Phoenix, Arizona Norfolk, Virginia Denver, Colorado DalIas/Fort Worth, Texas San Diego, California _ '1

Jack Faucett Associates, Ine. Final Report March 1997 _ _ Four general conclusions can be reached from the research and interviews conducted under this Task. First, many state-DOTs or MPOs do not have a lon~-t~rm vision Excite ret tm ~rr-~~~ ran Ions ~ _= ~ ^ ~ Gus-- ~ ·--A ~L tV Flwsl~ll~ lU1 uaLa Orgamzatlon. Fast and current data organization frameworks appear to reflect an individual agency's main responsibility based on the agency's planning requirements and planning duties. State-DOTs generally focus on maintenance of the state's transportation infrastructure and on the allocation of resources tor transportation projects. MPOs focus on data required to support long range planning and modeling functions associated, for example, with travel demand forecasting, demand management' and conformity analysis. Second, the coordination of responsibilities and efforts to respond to multimodal planning requirements has not been a focus of activity at the state-DOT or MPO level. For instance, a systematic approach to assessing data needs and organizing data across all user groups has not been implemented by planning agencies. Current data organization frameworks generally include the following components that reflect data uses: 1) System Maintenance and Plan Development, 2) Travel Simulation Model Development and Calibration, 3) Travel Simulation Model Validation, and 4) Plan and System Evaluation. Each component may require a different level of data aggregation, storage system, and dissemination strategy. Planning agencies, or departments within agencies, have focused data efforts within one of the data components. Even within the same planning agency, the same information is often collected and maintained by different departments, or groups, at disparate levels of aggregation and with different key variables (or identifiers) that cannot be reconciled easily across user groups. This causes duplication of effort and makes comparisons of data needs and programs difficult to conduct. Third. data needs are increasing in Ann. to the near nlanni~r~ I; ~ 1__ 1 ~ --= ~ r~~ ~ ~^-I_ ~11~, t~1~111~11~ ~ll~qut;llLly plEl{;mg ~ ~ . . . . . ~ . Iunner demands on data fathering .~orns>~ n~intr~n~nr~ anal a^al~re;e ~;~r;~;~^ ~+ toddle 61~ _-_ DOT and MPO levels. The response of agencies to new requirements has focused on the development of new travel demand models necessary for strategy evaluation and implementation. New analysis methodologies have led to the revision of current data organization frameworks to ensure that data needed to support new analytical tools are part of agency-specif1c data programs. However, this reactive approach to new planning requirements has done little to resolve the fundamental problem characterizing current data organization frameworks-specifically, the lack of coordination across agencies to minimize redundancy and optimize planning efficiency. A proactive approach to meeting the planning and data needs of multimodalism and the 1990 CAAA still has not been developed to date. = ~,3 -~.~45~3 ., ~1~ ~laly~l~ ~LlVlEl~5 ~t porn me slale Fourth, transportation planning traditionally has focused on passenger travel. ISTEA, however, and an emerging realization in policy analysis ofthe large potential public henefit.v otmr)~! ~nr~n~rnti~T1 : ~_ : _ ~ ~ ~ _ ~ ~ ~ _ ~ , ~ , Ire ~rc`crrn(JcccccJrezgnz transport, requires planning agencies to explicitly consider freight transport in the planning process. Freight data and modeling tools need to be collected and developed to meet this requirement. NCHRP-Mucizmodal Transportation 61 Project 8-32(5) Planning Data

Jack Faucett Associates, Inc. Final .R~por1; The following subsections address these four general conclusions in turn. 2.2.1.1 Planning Responsibilities and Data Organization March 19 7 As illustrated in Exhibit 18, individual planning agencies, or groups within an agency, currently focus on satisfying disparate planning responsibilities, and developing and maintaining data to support specific planning duties. Planning agencies may have applied a systematic, strategic planning platform, such as the Business Model, to their own localized goals and objectives. However, lack of coordination in the assessment of data needs and in the planning process has resulted in data organization frameworks that duplicate data collection, maintenance, and storage efforts across planning agencies. A review of agency-specific planning responsibilities and corresponding data practices is provided below. Federal Agencies-While Federal agencies are not directly responsible for statewide or metropolitan plarming, they may have a significant impact on the types of data collected for those purposes. First, Federal requirements require certain information to be collected at the state and local levels as inputs to national transportation databases. Second, the U.S. DOT plays a key role in the allocation of funds to state and local jurisdictions, and allocation is often based partly on information maintained in national databases. A number of national databases are described in the Directory of Transportation Databases developed by the Bureau of Transportation Statistics. National data are collected and maintained for decision-making issues related to funding allocation, the monitoring of resources, and transportation system status and perfonnance monitoring. Sfafe Agencies-The main kansportation-related responsibilities of states are system preservation, system expansion, and resource allocation to modal agencies and state jurisdictions. States, therefore, commonly organize data around the following functions: · system monitoring, which includes data on the physical status of roadways, bridges, and other facilities; · system performance, including safety data and data maintained in the Highway Performance and Monitoring System (HPMS); · system needs based on the estimated Gavel needs of citizens and commerce (e.g., socio economic baseline and forecast data); and funding allocation, which includes data on the cost-effectiveness of projects (referred to as performance measures) slated for implementation by jurisdictions. NCHRP Multimodal Transportation 62 Planning Data Project 8-32~5)

Exhibit 18 Views of Data Organization within the Planning Process 1 1 CURRENT / Mission 2 Fission 3 \ Agency __ _ Agency / MISSION it' Mission ~/ MISSION of/ / / - MISSION _ Agency ) ~ _ ,_ PUT Agency \ Agency ~ /Agerlcy J ISTEA MISSION Using Business Model to Coordinate Mission and Planning Objectives _ ~ Shared Data and Organization

Jack Faucett Associates, Inc. Final Report - A~arch 1997 Metropolitan Plal717ii2g Organizations-The primary responsibilities of MPOs include TIP and long range plan development and transportation-related air quality analysis. As a result, data collection and organization at the MPO level focuses on the development of information required to develop/calibrate, validate, and execute travel models. Other common MPO responsibilities include developing and maintaining local socio-economic data, both baseline and forecast data, required for travel forecasting and the allocation of Federal funds. MPOs generally organize data into the following categories. . Socio-economic data and other information required for the development of travel forecasts. Transportation network characterization data used to simulate current and future transportation services. Data for model calibration developed from surveys, external station traffic counts, auto occupancy information, and mode share information. Model validation data usually collected by the state arid local jurisdictions. Project evaluation data that are organized around funding categories. Operating Agencies-Operating agencies (including state highway departments, city departments of streets and traffic, transit authorities, port authorities, and private providers of freight and person travel) are responsible for the construction, maintenance, and operation of transportation systems. Information is maintained by these agencies on the physical and operational characteristics of transportation services, and operating agencies commonly provide data to MPOs which are employed for model validation and travel monitoring. Although these agencies do not typically provide information beyond the 2 to 3 year time horizon required for TIPs, operating agencies often identify and develop candidate projects for inclusion in TIPs. Data developed and employed by operating agencies are organized around an agency's decision-making process, focus on system performance, and represent a short-run horizon. 2.2.1.2 Components of Current Frameworks Various observations regarding current data organization can be gleaned from interviews and surveys conducted with state-DOTs and MPOs. First, while approximately one half of the MPOs surveyed in this study responded that data are organized around travel models (i.e., model development, calibration, and validation needs) and planning support (i.e., TIP and long range plan development and evaluation), many MPOs cited traffic monitoring as an important function requiring independent data systems. Second, little consideration has been given to a comprehensive and integrated data program, at least with respect to data organization and dissemination. Third, data maintenance and organization have evolved over time to address specific planning responsibilities, or specific requests for information. The planning agencies described above simply do not share a unified view of their NCHRP Multimodal Transportation 64 Planning Data Project 8-32~5)

Jack Faucett Associates, Inc. Fin al Report March 1997 - planning missions or responsibilities. The lack of coordination creates significant obstacles to achieving planning objectives, causes wasteful uses of scarce resources, and results with the development and implementation of conflicting transportation strategies. Exhibit 1 9 summarizes the generic data organization framework currently employed by transportation planning agencies. A detailed review of each data component is provided below. System Maintenance arid Platz Development-One objective of collecting and maintaining data at both the state-DOT and MPO levels is to monitor the present state of transportation systems, and plan for system maintenance and expansion on an as needed basis. As all~1~1 to in gP~:tir~n ~ ~ +hiO `~T^= she ~;~ ~1~ ~` ~ A. if_ _r `1 v'~= A_ r`~111~' IV1~ Vt a ll~llU~1 Q1 ally ~llmlagemem systems required by ISTEA (but later suspended). For example, the management systems that address system maintenance include: the Public Transportation Management System (PTMS), the Bridge Management System (BMS), and the Pavement Management System (PMS). To a lessor extent, the Safety Management System (SMS) and the Intermodal Management Svstem (TM~i Pro Plan r.nnr~m-A with the ,:_ ~` the transportation system. ~ , _ _ ~ , ~ ~.~__~.,_~ ~ ~.1~ ally Vat ~1 While development and implementation requirements of ISTEA's management systems are new, most operating agencies have been maintaining similar information for some time. Those responsible for the development and maintenance of the individual transportation systems, either by mode or function, maintain infommation related to system preservation. This infommation typically includes detailed data on the condition ofthe infrastructure, the physical extent ofthe transportation system (i.e., miles of roadway, track miles, etc.), the funding required to maintain the current system, and other data described in Section 2.1. In general, system maintenance and plan development information is focused on the short run; is concerned with past and current conditions; and is organized around the infrastructure and other units used for maintenance and decision making within the responsible agency-for example, bridge data are organized by bridge, rather than by the road segment the bridge is on (i.e., often different naming conventions and reference systems are used for bridge and pavement systems). {r"~,P1.~im1~1^f;~" - ~D' nD~1~f/~' ~ ~ ~ ~ ~ A-~} _-~,_- ~evr~--ve. 1~^V-~. ~=vCIV~UllU/U`io~-Another major category of information collected and maintained predominantly by MPOs includes data associated with developing and calibrating travel simulation models and analysis methods. This n.~(Jorv Of inn it . . ~. . . . . . ~ . . . t;OllLit;rn~U W1~ ae~ermmmg now 1nalvlaua~s Moe their travel decisions for personal or freight transport. Data collected for travel model development and calibration includes information from the Census' Jo7vrney-To-Work files, home interview surveys, special generator surveys, stated preference data, and other primary and secondary sources. Data contained in this component of the generic organization framework are disaggregate in nature, focusing on individual travellers, or shippers; are small in scale, including only a representative sample of individuals, trips, shippers, etc.; are concerned with travel choices of individuals or shippers and the rationale behind those choices; are concerned with trips or activities performed by system users, the timing of trios. and the origin-destination oftr~os: and characterize available choicer to fr~vPl`~r~ Ifs c, times, costs, etc.~. , ~_.- .,., mode-specific travel NCHRP-Multimodal Transportation 65 Project S-32(5) Planning Data

Exhibit 19 Current Data Organization Components System Maintenance & · Plan Development Plan & System Evaluation Travel Simulation Model DevelopmenVCalibration 1 --I- 1 Travel Simulation Model Validation

Jack Faucet' Associates. Inc. Final Report - Alarch 1997 - However, in some instances data cannot be collected at the individual traveler level. In those cases, data are still disaggregate in nature and characterize micro-level travel pattems (e.g., trips by traffic analysis zones, road segments, etc.). Nevertheless, data used for model development are organized so that changes in traveler behavior, or travel conditions, are related to other variables describing the physical and operational characteristics of the transportation system. This requires that common links be developed and maintained between data that describe the transportation system and data that describe travel over that system. Since one of the main functions of MPOs is to develop and maintain models used for travel simulation, MPO data collection and organization activities commonly focus on this data component. Travel Simulalior' Model Validafio'2-Data required for the validation oftravel simulation models are different in nature than those employed for model development. In the case of model development, disaggregate information is used to determine the relationships between the demand for passenger and freight travel and transportation system supply. In the case of model validation, data are collected for the purpose of calibrating models to ensure that travel models replicate actual demand and supply conditions. Data needed for model validation depend on a region's transportation problems and on the transportation strategies, or policy decisions, applied to mitigate the effects of such problems. Typically, however, needs associated with validation data are driven by data needs associated with travel models and methodologies used for strategy development and implementation. Traditionally, travel forecasting models have been used to analyze decisions concerning large scale infrastructure investments (e.g., the need for new highways, transit systems, etc.). Therefore, travel models have been validated using data on total daily traffic along major facilities. Similarly, travel models traditionally have concentrated on characterizing commute trips since these trips account for much of the peak congestion experienced in regions across the country. As a result, most infrastructure investments were focused upon reducing peak congestion. Changes in planning focus are also impacting the types of information that must be generated by travel models and, consequently, the types data needed to validate those models. For instance, demand management is much more of a concern, and reducing peak travel may not necessitate the construction of new facilities nor capacity expansion. The move to transportation efficiency planning is pushing modeling to determine travel by time-of-day and to determine the effects of demand management strategies on traveller behavior. Therefore, among other changes, information needed for model validation needs to be expanded to differentiate traffic volumes by time-of-day. In general, this component of the current organization framework is characterized by the following: · the validation process is designed to check aggregate statistics of system usage that can be verified by model outputs (e.g., average trip distance, average daily volumes, cordon counts, etc.); NCHRP Multimodal Transportation 67 Planning Data Project 8-32(5)

Jack Faucet' Associates. Jnc. Final Report March 1997 data are organized around transportation services and facilities. modeled accurately; and the use of which must be data are often obtained from external agencies, such as information maintained by operating agencies. MPOs are the principal users of model validation data. However, MPOs often depend on others for the collection and maintenance of validation data, including data on traffic counts, transit ridership, parking' and tripmaking. Plait and System Evaluation-Data are also collected and maintained by state-DOTs and MPOs to evaluate transportation plans and systems. Typically, this requires information on the composition of future projects, on the timing of project implementation, and on project costs. Both MPOs and state-DOTs are involved in developing and maintaining the data needed for project analysis. However, data programs for this purpose are seldom coordinated between the relevant agencies. This component ofthe data organization framework also includes information required to produce future travel forecasts, such as land use forecasts, socio-economic and demographic growth estimates, expected employment attractions, and travel cost forecasts (e.g., the price of fuel, parking, transit, etc.~. Furthermore, this component addresses various transportation supply issues. For example, information on future transportation services and facilities, represented in computer simulation models, fall under this component. Unlike analysis describing current travel conditions, the process of forecasting travel must also account for alternatives in both land use and transportation services and facilities. Often, this requirement is overlooked in the organization of data describing current conditions. This does not allow future alternatives and scenarios to be developed, maintained, or easily compared. Information for plan and system evaluation includes both forecast inputs and outputs of the analysis methods; accounts for multiple funding alternatives and scenarios; and includes data on project funding' scheduling' arid implementation. 2.2.1.3 Responses to New Planning and Data Requirements The change in planning focus is motivating the development of new analytic tools necessary for the development, evaluation, and implementation of transportation strategies that enhance system efficiency. Planning agencies have responded by augmenting current data organization and collection practices to account for data needs associated with new analytic tools. This reactive approach, however, has done little to resolve the fundamental data coordination and integration problems that characterize current frameworks. The focus of activity on the pert ofplanners toward changing modeling paradigms reflects the process-based philosophy that has dominated the practice NCHRP-Multimodal Transportation 68 Planning Data Project 8-32(5)

Jack Faucett Associates, Inc. Final Report March 1997 in the past. Clearly, this focus ignores various other important data components, such as system preservation and expansion. and does not arl~lr~.~ the nil of 1r~nc!n~rtatir~^ ~lq~.~ ~ : A ·1_ _~ ~ ~ ^~ v ~^ ~Vl~I=ll ~J1Ci1111111~ a~cll~l~5 anal focus on supply issues. Furthermore, this focus does not address the multimodal aspect of ~ e 1 e ~ . ~ . _ _ transportation, since rail, air, and water modes are ignored by travel demand models In the short run, enhancements to the 4-step travel demand modeling framework that address immediate policy issues are imposing significant demands on the types of data collection activities undertaken by MPOs. These demands, in turn, directly impact the travel model development, calibration, and validation components of current data organization frameworks. Changes to the 4- step process include the following: the development of feedback mechanisms to account for the impact of transportation on land-use decisions; the interaction of travel demand models with air quality models; the effect of TOM strategies on travel demand and emissions; the inclusion of non- motorizedmodes of travel; the interface between regionalmodels end traffic simulation models; and the effect of Intelligent Transportation Systems (ITS) on traffic patterns. In the long run, travel models are expected to accommodate dynamic traffic assignments, to be based on transportation-related activities rather than trips, and to interface between passenger and freight transport. In the distant future, the travel modeling philosophy is expected to move toward micro- simulation algorithms that mimic behavior of travelers and shippers (freight). Short-term and long-term changes in travel demand modeling, and associated data organization issues, are described below. Feedback in the Four Step Model Process (Short-Term Change)-Feedback through the trip distribution, mode split, and traffic assignment steps of the four step model results in trip tables as close as possible with the input tables, and travel times as close as possible with those that would have resulted had the input trip tables been assigned to the networks. As such, adjustment for feedback is a mechanism which guarantees internal consistency among the three steps of the model. The data to implement such a procedure are not fundamentally different from those needed in the conventional sequential model process. Hence' Hey will continue to be nr~ani7:~f1 into o~.nor~nhi~1 coverage' network databases' and survey data. ~_ =_~^ ~.~ The feedback process begins with an exogenous trip generation database which links land use and socioeconomic factors that affect travel demand to determine the trip ends inputs to the trip distribution step. Data elements organized for the purposes of the trip generation model step include: the number of households by TAZ, the average household size for TAZ, the average household employment for TAZ, the trip generation rates (daily person trips) for workers (home to work, home to non-work, non-home to non-home) and non-workers (home to non-work, non-home to non-work) by location ofthe household, auto ownership status, licensed drivers, number of household members under five years old, and over five but under 1 6, age of head of household, occupation of head of household and other workers, marital status, housing type, home ownership status, length of residence, and distance from the CBD. Moreover, a number of variables are usually obtained from NCHRP-Multimodal Transportation 69 Project 8-32(5) Planning Data

Jack Fauceu; Associates, Inc. Final Report _ March 1997 . secondary sources and survey data. Typically, such variables include the populations of manufacturing, service, retail, etc. employment by TAZ, and trip rates based on floor space for Central Area trip generation. The feedback process iterates among the trip distribution, mode split, and traffic assignment steps until consistent estimates of travel times and modal shares are computed. The combined model calibration is based on observed (or estimated from a different process) base year data which are not data input in the model. Typical examples of data used in the calibration process include: origin- destination-mode trip tables for the morning peak by trip purpose, peak hour factors by zone, commercial vehicle trip tables, transit fares, etc. Furthermore, additional data are needed to evaluate the model and perform sensitivity analyses. Different evaluation measures are used to test the accuracy of the calibration method. Evaluation measures consist of data not used during the calibration. Typical examples include regional transit shares, transit shares to COD, regional means ofthe modal travel times and costs, mean trip lengths for all trips or trips by mode. If large enough samples are available, then it is common (especially for choice models) to split the data into two halves and use the first one for estimation and the second one for validation. Air Quality Inputs (Short-Term Change)-Separate traffic assignments by time of day are needed for air quality analysis. The specific time periods very much depend on the needs of each region. In particular, an area with a CO problem may need to devote its modeling resources to the afternoon peak where CO concentrations are higher. On the other hand, VOCs and NOx emitted in the morning peak have a longer time to react to light; thus morning peak assignments are mostly relevant in those areas with VOCs and NOx problems. In addition, ozone concentrations peak during the late morning or late afternoon periods. Unlike CO, however, NOx is a product of efficient combustion and so can increase as CO decreases. Transportation planning can influence air quality planning in at least two aspects: a) based on projected emissions factors, demographic and travel forecasts, and assumed future highway and transit networks, a mobile source emissions inventory is estimated for a target year at given time intervals; and b) transportation controls are included as necessary to show reasonable progress of some pre-specified air quality set of requirements. If air quality planning, TOM and TDM planning and transportation models are to be coordinated it is also important that an unambiguous estimate of net benefit per unit of pollutant removed be defined. This is because TCMs often yield multiple benefits and entail both direct and indirect costs which vary over time. In addition, new emission factors may alter the assessment of emissions reductions from TCMs; existing methods underestimate mobile source emissions. In air quality modeling, interfaces are developed between the transportation and emissions modules. These two modules have been developed along separate lines of research. As a result, the communication between the two can only be achieved after post-processing output from the NCHRP-Multimodal Transportation 70 Planning Dana Project 8-32¢5)

Jack Faucett Associates, Inc. Final Report March 1997 transportation model to make it compatible with the input requirements ofthe emissions model. Post processing provides estimates of hourly traffic volume and speed by vehicle type on each link of the network for a particular year and scenario (build/no build) which are then used to determine emissions estimates and evaluate policies affecting commute. ITS technology can be used to effectively monitor traffic-related air pollution levels in an urban area. A prototypical system for such a task may be comprised of different modules for environmental monitoring (for on-line verification of pollution levels), continuous monitoring of traffic flows, regularly updated, short-term meteorological forecasts, air quality models to estimate expected traffic pollution levels, off-line determining the likely effects of alternative control strategies and communication technology to implement control strategies. The impact of deploying an ITS system is twofold. First, planning and implementing alternative policies is substantiated by model outputs that depend on accurate data. An ITS system is expected to provide a comprehensive data inventory of pollutant emissions in addition to key information on compliance rates and actual effectiveness of existing rules. Second, mobile source estimates provided by existing models (e.g., EPA's MOBILE model) are known to have large standard errors because a number ofnecessary variables are not well estimated locally. The growth ofan urban area results in an increase in both area and mobile pollution sources the impact of which is impossible to separate satisfactorily with today's models. A suitable ITS system is expected to provide very accurate mobile sources data which can then easily be contrasted to area sources data and support pollution containment strategies more effectively. Lard Use Transportation Interaction' (Short-Term Change)-The four-step model has been used to help in analyzing the sizing of capital facilities (especially highways) and in corridor planning analyses. While very problematic in the first case and less so in the second one, in both cases it is assumed that the pattern of activity location in the region is not affected by transportation improvements. However, when transportation investments are anticipated to spur growth it is necessary to include interaction between land use and transportation in a regional model. The addition of a new facility in a future year network is assessed for its impact on land use by feeding back the congested travel times into the-land use model and reallocating households and employment to TAZs. Data requirements include land use by type, lagged and un-lagged zonal employment by sector, lagged and un-lagged zonal percentage household composition by income quartile, and zonal impedance matrices. The land use module on such an interactive land use and transportation model measures economic growth by an employment indicator which is typically forecasted first. Forecasts of the regional economy are based upon state control totals for economic activity, which totals often are based, in turn, on federal projections of regional economic activity. Population forecasts similarly are based on federal/state estimates. Typical data used include comparative shares and growth rates of industries by the Standard Industrial Classification (SIC) code, and/or data on earnings, income, land NCHRP Multimodal Transportation 71 Planning Data Project 8-32~5)

Jack Faucets Associates, inc. ~e~ larch 1~°,°, and housing prices, cost of money, resource prices, construction costs, taxes, birth rates and life expectancies by ethnic group, and indicators for quality of life (sewer, water, and other infrastructure, crime rates, etc). Given all the potential sources of uncertainty, each transportation policy scenario would be analyzed for optimistic, moderate, and pessimistic employment and population scenarios. TCM/TDM/Pricing Analysis (Shorf-Term Change)-The primary purpose of travel demand management (TDM) measures is to reduce the number of vehicles using the road system, while providing a wide variety of mobility options to those who wish to travel, and the supporting strategies that encourage the use of alternative modes of travel. TDM programs may include short- term actions (aimed at solving the more immediate issue of too many cars in one place at one time), or longer term congestion avoidance strategies. There is an obvious link between TDM strategies and land use/management growth strategies since exercising control over the trip generating characteristics of the land use can be used to make the resultant demand consistent with the existing transportation infrastructure and the level of service desired. Transportation control measures (TCMs) on the other hand, are a core set of actions designed to improve transit levels of service, support ridesharing, and build upon the special relationship between employer and employee to implement measures that make driving alone less attractive relative to other modes. As such, TCMs aim at reducing vehicle trips and VMT, and increasing average vehicle occupancy and speeds. There is an immediate connection between TCMs and emissions although it is difficult to separate the direct and indirect effects of TCMs. It is doubtful that it is possible to accurately estimate the net effects of TDM and TCM programs without explicitly accounting for such programs in regional models. Even then however, a number of issues would remain unresolved. There is wide disagreement, for example, over the health effects attributable to various atmospheric pollutants and the equity implications of such programs. Readily available transportation control strategies mostly address the home-to-work trip. It is generally recognized that TOM emissions reduction potential cannot be much greater then 5% without some way of addressing non-work and commercial travel. No'2-Motorized Travel (Short-Term Change)-Traditionally urban travel demand models do nor address travel by non-motorized modes due mainly to the lack of data. Large origin-destination surveys seldom ask about bicycle and walk trips, and if they do the numbers are small enough and do not permit separate estimation. Moreover, short walk trips are usually under-reported. Although the problem of small data samples is common in the case of transit trips in many areas, transit riders can be surveyed separately; this would be impossible for pedestrians or cyclists. If there exist enough data to estimate a combined or separate walk and bicycle mode (or mode of access to other modes) then it may be useful to develop some sort of accessibility index for this new mode(s). This index may require information on the quality of sidewalks (present/absent, narrow/adequate) on major arterials within and outside the CBD, the land use mix for high and moderate densities, the building setbacks, the transit stop conditions (presence or absence of NCHRP Multimodal Transportation 72 Planning Data Project 8-32(5)

Jack Faucett Associates, inc. Pin al Resort _ . March 1997 . shelters), the bicycle infrastructure, the ease of street crossing, the connectivity of street sidewalk system. The inclusion of non-motorized modes into travel models will allow the evaluation of programs such as the conversion of an existing lane into a commuter bicycle lane, the addition of a commuter bicycle lane, the re-striping of roadways to create bicycle lanes, the development of a limited access bicycle network, the provision of bicycle facilities on passenger ferries, the building of parking lots for bicycles, the potential for bicycle purchase subsidies, the widening of sidewalks in commercial districts or the construction of new sidewalks, the construction of pedestrian space and amenities including benches, kiosks, and pedestrian guard rails connecting pedestrian and bicycle barriers between cur-de-sacs, the improvement of crossings (curb bulbs, raised crosswalks, increased crossing time), etc. New Trip Purposes, Trip Chaining (ShorI-Term Change)-The commute trip is no longer the most significant contributor to urban congestion. With more single parent and two-worker families, trips from home to work and from work to home have increasingly become chained trips, with intermediate stops to drop offpassengers, shop or conduct personal business. Traditionally, travel models do not account for trip chaining. Incorporating trip chaining into travel forecasting models would benefit the estimation of non-work travel, which, despite accounting for the largest share of urban area travel, is very difficult to model. The development of activity-based models will be benefitted as well. The data requirements for immediate inclusion of trip chaining into regional models are home interview travel diary surveys. To develop models that analyze the full effects of trip chaining on travel decisions an enhanced travel diary survey with increased detail consistent with the development of activity-based models will likely be needed. Track c Simulafion/Regio'2al Models Interface (ShorI-Term Change)-Regional models simulate the macroscopic traffic flow in highway and transit networks. Traffic simulation models run on a much more detailed network representation. Various road groups used in the regional models (e.g.,- urban interstates and freeways, urban principal arterials, urban minor arterials and collectors, rural interstates, rural principal arterials, and rural minor arterials and collectors) can be further categorized in traffic simulation (e.g., mid block urban/rural freeway link, ramp approach urban/rural freeway link, on/off ramps, mid block arterial link, intersection approach and departure link, and links with intersections at either end). Moreover, in regional models information on traffic signals or traffic control devices is almost never represented. There is need to develop an interface between regional and traffic simulation models for improved environmental and traffic engineering modeling. A post-processor, for example, could feed the output from a regional model into a traffic simulation model where volumes are adjusted so that the speeds from node to node are consistent with the average speed from the post-processor. Speed variations within the same link can be further simulated by breaking down individual links into NCHRP-Multimodal Transportation 73 Planning Data Project 8-32(5)

Jack Faucett Associates, tuc. Final PeeoH March 1997 smaller segments, so that speed profiles for each link group can be obtained. Acceleration deceleration, cruise, and idle phases can be then identified. Speed profiles can be further fed into an emissions model for more accurate emissions estimates. An additional use for such an interface would be the investigation of queue spill-back effects on route choices. 1, ITS and Real Time Simulation (Shorf-Term Change)-Transportation models need to account for different rates of penetration of emerging ITS technologies into the market. Such issues can be examined if separate trip tables for different classes of travelers for different levels of access to information regarding travel decisions are prepared. On the other hand, ITS encompasses a very broad spectrum of applications extending far beyond the purposes of travel models. For example, ITS can provide travel and transportation management and en-route driver information (i.e., route guidance, traveler services information, traffic control, incident management, emissions testing and mitigation), travel demand management (pre-trip travel information, ride matching and reservation, demand management and operations), public transportation operations (public transportation management, en-route traffic information, personalized public transit, public travel security), electronic payment (electronic payment services), commercial vehicle operations (commercial vehicles electronic clearance, automated roadside safety inspection, on-board safety monitoring, commercial vehicle administrative processes, hazardous materials incident response, commercial fleet management), emergency management (emergency notification and personal security, emergency vehicle management), and advanced vehicle control (longitudinal collision avoidance, lateral collision avoidance, intersection collision avoidance, vision enhancement for crash avoidance, safety readiness, pre-crash restraint deployment, automated highway system). These applications of ITS will most certainly require data from various sources. Moreover, these new voluminous data requirements will motivate the development of new data organization frameworks. An interesting question for the development of ITS, in addition to the technical challenges, is whether ITS should be devised in an integrated manner to achieve the synergistic goals of an integrated system (a top-down approach), or as loosely coupled systems that can be more responsive to market preferences and opportunities. An argument for the top-down approach is that system- level technological changes that incorporate, for example, advanced vehicle control systems, may not come about in the absence of a broad scale architecture. Thus, a failure to develop system architecture may be essentially an abdication of the potential of ITS to effect system-level-changes. On the other hand, a top down approach may be inappropriate to the nature of ITS technology. Systems designers may be out of sync with broader market forces that favor the development of specialized, niche-oriented technology. It may be possible that ITS technologies will be de-col~nled ~ ~ ~ ~ ~ _ _ ~ 1 1 ~ ~ . ~ . ~ , ~ . . . llVlil All UV~lall Uall~pVl=LlVll Vl~lUll ·O locus me necrology as a mechanism to achieve other goals, such as using automated vehicle identification to develop market-based encroaches to improve air quality. In the long term, it is expected that travel models will change considerably to accommodate dynamic traffic assignments and activity-based models, and to create an interface between goods movement and passenger travel. Moreover, in the even longer term the modeling philosophy may change NCHRP-Mu~imodalTransportation 74 Project8-32(5) Planning Data

Jack Faucett Associates, Inc. Final Report March 1997 . dramatically by moving away from established modeling paradigms towards large scale simulations. As a result, existing data organization frameworks will be challenged. Dynamic Assig'2mer~t (Long-Term Change)-Common characteristics of currently used traffic assignments is that they estimate link volumes for an entire time period (morning peak, afternoon peak, average weekday) based on the trip tables for that time period. Furthermore, the BPR volume- delay function does not take into account intersection related factors such as traffic signal timing and phasing and the presence and adequacy of turning lanes. Moreover, interactions between links are still very difficult to introduce in large networks especially if there is need to consider merging and weaving effects on freeways, and queuing effects on both arterials and freeways. In dynamic assignments the analysis period is subdivided in several intervals and the demand is computed as departure rates for that time slice. Trips are assigned during each time slice from their origin toward their destinations. Each vehicle traverses the network only as far as the vehicle could travel during the time slice. Trips which did not reach their destinations during the previous time slice continue from the points they reached previously. Many trips, including vehicles still waiting in queues, may not be completed by the end of the analysis period. This represents congestion that spills over from a peak period to a subsequent period. Activity Based Models (Long-Term Change)-The basic hypothesis underlying travel demand models postulates that the demand for transportation is derived from the demand for activities such as work, shopping, personal business, and recreation. Development in information technology and increasing levels of congestion are expected to enable individuals to pursue activities without traveling, and change individual activity patterns due to the reallocation of time and resources previously used in travel. The new generation of activity-based models are expected to be sensitive to changes in transportation and information technologies and thus, incorporate no-travel options to capture the changes in the activity patterns of individuals and households. Activity-chaining mechanisms will be incorporated into the new models and the main factors and constraints involving individual and household activity decisions will be identified and implemented. Moreover, the analytical tools to capture the dynamic and adaptive behavior will be developed. The databases required for such developments will result Mom stated-preference surveys, simulators and field tests. In the longer term, activity-based models will need to address telecommuting, the effects of ITS on the dynamic travel choices, and in-home and out-of-home activities. The data needed for such modeling tasks vary from ITS related data collection efforts to data collected from actual implementations of different teleoptions. Goods Movement/Person Travel Interface (Long-Term Change)-The movement of goods is a very complicated process which is increasingly seen as part of a broader process of logistics and management. To improve urban goods movement it is necessary to understand the nature of the NCHRP- Multimodal Transportation 75 Planning Data Project 8-32~5)

Jack FaucettA~tsoci~es, Inc. Final Report Starch 1997 demand for goods and the factors affecting it. Freight movement incurs substantial level of congestion the cost of which is passed on to customers. Moreover, there is a considerable concern about the adverse impacts of freight movement such as noise, emissions accidents. ha7~r(1n~lc pronely storage and distribution, etc. T^~^ ~u ~ ~A _~1__ ~1 ~1 · 1 - ., _ I, ~ ~ Issues such as the use ot mgh-productivity vehicles (e.g.3 twin and triple-trailer trucks) the efficiency of intermodal terminals, the traffic requirements of trucks, the location of commercial activities, the pricing of road usage will need to addressed in the longer term. There are fundamental differences between the movement of people and the movement of goods. As a result established travel models for passenger modes cannot accommodate satisfactorily the complexity inherent in the movement of goods. The route choice behavior of a truck driver, for example, is considerably more limited than that of commuters. The behavior of decision makers for goods movement m~v n~f f~Yhihit the C.~Q level of rationality anticipated by personal travel. To develop an interface between passenger and freight travel forecasting it may be possible to use GIS as a platform for data management, integration and display and develop an intermodal freight visual database. Existing public freight data sources include databases at the federal (e.g., aggregate market totals), state (e.g., registration, vehicle classification, specialized information on local industry), and MPO (e.g., various forms of surveys, periodic information) levels. There is clearly a need to integrate into those sources shipper data as well. ~ ~^_~d ^~ ~ _~ vet ~ ~ Cam TRANSIMS (Lo'2g-Term Change)-In the long run, it may be possible to do away with established modeling paradigms and move toward large scale simulation efforts. This is evident with respect to transportation model developments. Such efforts are aimed at fully integrating land use, transportation and air quality models and have the following key elements: representation of individual travelers and freight; representations of environmental and vehicle characteristics, simulation of continuous traffic, transit, freight, bike and pedestrian travel patterns over an extended period of day' as well as different days of the week, months and seasons. TRANSIMS is the only example of a fully deployed very large scale simulation effort in the U.S. TRANSIMS is a system of linked modules including: a) emissions modules to consider evaporative emissions, cold-start emissions and high-emitting vehicle emissions, b) regional-scale meteorological models able to incorporate the effects of fog and clouds, c) microscale meteorological modules (street-canyon), d) transport and dispersion modules, and e) air chemistry (airshed) modules. TRANSIMS is drawing from experience with regional scale meteorological models that can describe airflow and turbulence driven by terrain and land use without the requirement of many local measurements. Stationary and mobile emissions are separated given the availability of individual travel plans that describe when vehicles are used. The data flow among different modules of TRANSIMS may be summarized as follows: NCHRP-Multimodal Transportation 76 Project 8-32(5) Planning Data

Jack Faucett Associates, Inc. Final Report . Marek 1997 given sufficient demographic data, synthetic populations of households are created at the desirable level of detail and distributed to match real regional data; · household activities, activity priorities, activity locations, activity times and mode and travel preferences are generated; . individual travel plans are simulated and the demand for travel is determined and assigned second-by second; and positional data on velocities, accelerations, decelerations, average speeds and average travel times are determined and fed into the emissions model to produce NOx, CO, aerosol and hydrocarbon emissions. Summary of the Effects of Changes on the Current Data Organization Framework-The changes described above are stressing current data organization frameworks and data collection activities. First, new data are required for the development of new modeling tools that build on the conventional 4-step travel modeling process. For example, surveys are being executed to accumulate time-of-day travel detail for temporal travel models. Likewise, data on non-motorized trips are being collected necessary for the execution of non-motorized travel models. Changes such as these are augmenting the Travel Simulation Model Developmer~t/Calibration and the Travel Simulation Model Validation components of current frameworks. Furthermore, air quality analyses (e.g., TOM and conformity analyses) highlight the need for expanding current data organization frameworks to account for transportation impacts. Current frameworks embed impact data within the conventional components described in Section 2.2.2. Exhibit 20 exemplifies how short-term and long-term changes are affecting current data organization frameworks. Changes to current frameworks, however, will not address fundamental problems associated with data coordination, organization, consistency, and integration. A pro-active approach to developing data programs is required. 2.2.1.4 Freight Transportation Planning and Modeling Needs The United States currently spends over $400 billion annually on freight transportation, approximately 60 percent of what it spends on passenger transportation. Approximately 30 percent of the total freight expenditures are for local drayage and the remainder is represented by about 3 trillion ton-miles of intercity freight movements (contrasted with slightly over 2 trillion intercity passenger miles). In spite of the obvious importance of freight transport in the U.S. economy, planning resources applied to goods movements analysis and forecasting by public agencies are quite small compared to those devoted to passenger travel planning. There are perhaps two principal reasons for this. First, the expenditure comparisons between freight and passenger travel do not represent the relative NCHRP-Multimodal Transportation 77 Planning Data Project 8-32~5)

Exhibit 20 Effect of Planning Responses to ISTEA and 1990 CAAA on Current Data Organization Framework Strategy & Impact Assessment - Air Quality Inputs - Land Use Interaction - TDM/TCM . r . ~ System Maintenance _ . & Plan Development Plan & System Evaluation - - Note: Long-term changes alter the entire framework, as does ITS. Short-Term Modeling Changes - Feedback - Non-Motorized Travel - Trip Chaining - Model Interfaces 1 . ~ - Travel Simulation Model  DevelopmenVCalibration Travel Simulation Model Validation . . 1 1 1 1 .

Jack Faucett Associates, lace Final Report Uarch 1997 resources expended, since passenger travel involves the resource cost of the time spent by the passenger which is not reflected in the expenditure data. If the personal costs of travel time were included, total resource costs of passenger travel and delay times would be increased by several hundred billion dollars. Second, the problems in passenger travel (e.g., inefficiencies and congestion) are more visible to the public (since they are directly involved) than similar problems in freight transportation which are largely hidden from the public since costs are passed through only indirectly to the public in the prices paid for finished products. Thus, public officials are subject to greater pressure to correct inefficiencies in the movement of people than in the movement of goods. Perhaps a further reason is the predominance of the highway mode in both freight and passenger movements, and that the use of public highways for freight movements is concentrated in the business sector as contrasted with the household sector in the case of passenger travel on the highways. The business sector does most of its own logistics planning whereas households deDend 1mon n~lbli~. Frail In Err+ ; interest in facilitating passenger travel. · __ ~^ Id V~l~l~ t~ 1~pl~llt 1~ The lack of consistent sets of data on freight movements has hampered modeling efforts. This is more of a symptom of the lack of recognition of the need for freight analysis and forecasting than a cause, however. Effort and resources could have been applied to develop ~P~ll~tP oh if freight movements had been accorded a higher research priority. ~-r-- -A-If i' In view of the lack of data, the heterogeneous characteristics of freight movements among disparate modes and the lack of adequate models to tie freight movements to activities that generate these movements, perhaps there should be little wonder that freight transportation analysis has not been a more prominent focus in transportation planning. This has changed with the passage of ISTEA in 1991, legislation which recognized the need for greater cooperation among the modes in freight transportation planning. Many states are now developing an Intermodal Management System which encompasses both freight and passenger movements. However, intermodalism in freight movements had already been recognized for its efficiency potential with the trend toward containerization which Rev fq.~ilitat~e the Age ~( freight between modes. ~e~-_~} ^~ ~O ~1 ~L1 ~1~1~1 Q1 Most of the freight transportation modeling has been performed at the national and state level, focused on inter-city freight movements. Only in the last several years has much effort been concentrated on urban goods movement. The national arid state models are largely devoted to forecasting height movements for Me implications for additional inDas~ucture needs. Other models i8The highway mode accounts for approximately 80 percent each of the national passenger costs and the national height costs (Eno Transportation Foundation, Inc., Transportation in America, 12th Edition, 1994, pp. 40, 42. NCHRP Multimodal Transportation Planning Data 79 Project 8-32(5)

Jack Faucett Associates, Inc. Final Report March 1997 have been developed for modal split or modal choice analysis for inter-city freight movements, largely at the transportation corridor level. Intercity Freight-Analysis and forecasting of intercity freight movements can serve several purposes. National planning for infrastructure improvements and additions to the proposed National Highway System (or the proposed National Transportation System) and state planning for other principal state ar~terials. State and MPO planning for routing of large trucks entering and departing the urban area (e.g., the Alameda Corridor). Intercity trucks account for a relatively small percent of vehicular traffic in urban areas, but contribute disproportionately to urban congestion and pollution emissions (in urban transportation planning a large truck is generally considered equivalent to 3-5 passenger vehicles in its effect on congestion). National, state and local planning (public and private modes) for location of, and access to, intermodal connectivity and transfer terminals. 4. Research and planning activities to reduce emissions from freight vehicles (trucks and locomotives) and vessels. Models for forecasting inter-city freight movements may loosely be grouped into either statistical models based on the analysis of time series data, or deterministic models based on economic input- output relationships and supply/demand origin-destination patterns. These principal types are described below together with their data needs. Statistical Models and Data Needs. Statistical models for projecting inter-city freight flows range from sophisticated regression models to simple time-series extrapolation models (e.g., exponential smoothing, ARIMA). Most have dealt with aggregate commodity movements of the gravity type in which flows between nodes are a function of the mass of production and consumption at these nodes and the distance or transportation costs between the nodes. The more complex statistical regression models incorporate explanatory variables such as limited detail on the types of commodities produced and consumed at each node, costs of transport and salient characteristics of the transport network connecting the nodes. These latter models include modal choice models with explanatory variables that represent modal networks and their operating and service characteristics, transport costs, as well as specific freight characteristics and institutional factors that affect shipper decisions on modal choice. These models require input data on commodity flows for a historical period, or cross-sectional data, with sufficient observations to fit the parameters with an acceptable degree of confidence. The problem of course is the lack of time series or cross-sectional data at the node-to-node level (e.g., NCHRP- Multimodal Transportation 80 Planning Data Project 8-32(5)

Jack Faucett Associates, Inc. Final Report March .1997 BEA-BEA pairs). Selected data on rail and inland waterway movements are available from the one- percent Carload Waybill Sample and the Corps of Engineers waterborne commerce statistics~9, respectively, but trucking commodity flow data are not generally available except through special surveys which are expensive and infrequent. (Data on pipeline shipments and limited data on air shipments are available, but are of limited relevance to most planning agencies.) The rail Waybill data are subject to disclosure suppressions that may be avoided through confidentiality agreements; however, the sample is not sufficient to represent all significant shipments at the BEA-BEA level of detail. Even if data were bountiful to fit these statistical models, a number of limitations of such forecasts should be kept in mind. The forecasts are not controlled to any overall forecasts for the U.S. economy that reflect sustainable growth over the total economy. it. The commodity-m~x In the forecasts implicitly reflect static patterns (trends) in commodity- mix which are changing with changes in the composition of GDP and foreign trade. 3. The origin-destination patterns of shipments in the forecasts reflect static patterns (trends) rather than the dynamic shifts resulting from demographic and economic competitive forces. These statistical models are applicable in developing finer area detail within the framework of a national freight flow model (described below). National models are developed to provide data for analyzing and forecasting long-distance freight movements, controlled to be consistent with overall national economic growth and its regional distribution. They become unmanageable if area detail, below for example the BEA level, is included. Planning below this level is performed more efficiently at the regional level, but it should be consistent with national economic growth. National and Interregional Freight Flow Models and Data Needs. The projection of inter- regional freight commodity flows needs to be approached from an economy-wide perspective to provide a forecasted database that is internally consistent and one which state arid local plarlIiing agencies can use as control tows in developing fiercer detail as needed in specific area planning. The reasons for this are: 1. Trade in commodities is nationwide (and global) and is intricately tied to economic production arid consumption (demand) across the nation (and internationally). i9Special tabulations of point-to-point data from the waterborne commerce statistics may be obtained from the Corps Data Center in New Orleans subject to disclosure suppressions. NCHRP Multimodal Transportation 81 Project8-32~5) Planning Data

Jack Faucett Associates, In e. Final lteport March 1997 A. Projections of trade developed at the state and local level are necessarily limited in forecasting freight in, out and through the area unless a national framework is used, resulting in forecasts of transportation infrastructure needs Mat are not consistent with forecasts made by adjoining and other states. 3 Tat ~ ~1~,_1~:~ _ _- · ~ _ ~ _ ~ 1 ~ . ~ . _ 1 ale `;~S 01 developing a nallonw~ce database are substantial at 5-year intervals, the cost depending on whether state or BEA detail is provided. It is believed this cost would be significantly less than expenditures now made by states and MPOs in improvisation in filling the need for estimating and projecting non-local originating and destinating freight in current development of Intermodal Management Systems. Furthermore, having been involved in some of these ad hoc efforts at the state level, we are confident the national database would be far superior for purposes of state and local planning than those databases now developed. A national approach to freight transportation modeling and forecasting is based on the analysis of activities in the economy which generate the freight movements. This involves the use of economic input-output databases which detail the production in each region and consumption in each region. Consumption includes the inputs of commodities needed to support production and final demand in each region (household and government consumption, investment in plant and equipment and foreign exports through exit ports in the region). Freight flows (and flows of services) connect the regional economies so that demand in each region for each commodity group (production inputs plus final demand) is satisfied by production in the region less shipments out ofthe region plus shipments into the region from other regions and from foreign imports. The database is implemented for a base year and requires updating periodically (probably every five years) to reflect shifts in production sources, changes in the mix and level of commodities demanded in each region, and changes in the O-D (origin/destination) patterns of commodity shipments for whatever reason (e.g., changes in relative transportation costs by or~gin/destination and shifts in production sources). The system represents a set of simultaneous linear equations, one demand equation for each commodity group in each region plus a transportation equation for each commodity which distributes this demand to regions that supply this commodity. The demand equation for each commodity specifies demand es a function ofthe outputlevel of each producing industry in the region (the input- output coefficients), plus final demand (which is stipulated in a forecast and is the "driver" of the system). The transportation equation distributes the demand to producing regions in fixed proportions to the base year sources-this assumption that base year O-D patterns hold for a forecast of course is a basic weakness in the model and must be adjusted for in any longer term forecast. These adjustments are based on any information available on prospective shifts in production sources, especially for natural resource based minerals, forest products and agricultural products. NCHRP Multimodal Transportation Planning Data 82 Project 8-32(5)

Jack Faucett Associates. Inc. Final Report Marok 1997 - Longer run shifts in population and the work force also will affect the ~roductive ~,onqhilit~r ~fq given production source.20 Or-- a,.. i, The system is driven by forecasts of final demand. Household consumption constitutes approximately two-thirds of final demand -- and investment in infrastructure and plant and equipment, and inputs to government activities are at least significantly related to population, labor force and income. Projections of population, labor force and income are generally the cornerstone of final demand projections. Given the weak status of economic development theory, these projections are generally the cornerstone of economic growth forecasts by region, and are routinely developed by the U.S. Bureau of the Census, The Bureau of Economic Analysis, the U.S. Bureau of Labor Statistics, state and local government agencies and private organizations. (The problem is not the lack of sources of these projections, but which ones to select.) This discussion simply points out that the "driving" force of the system outlined is already well supplied with forecasts to which considerable resources are already devoted. A database of interregional input-output accounts, with detail for each state, and balanced with state- to-state freight flows in commodity detail, was constructed for 1977.2' The inr)ut-nutn~t m~trir.~e r^~ arch Eve= Tom Art =~ .,_ AN ~ _~ ~ 1 ~ ,~v~ amp.. ant vV~l~ "~V~lUp~U llUll1 WOl1~U~ =1U vamp source data on production in each state and input data specific to the state sectors and final demand as imputed from the national I-O table developed by the Interindustry Economics Division, Bureau of Economic Analysis, U.S. Department of Commerce. The freight flows connecting the state economies were developed from the 1977 Commodity Transportation Survey, the rail Waybill Sample data, and other sources. The system was balanced (with estimated adjustments) so that production plus shipments into each state balanced consumption plus shipments out from each state. This balancing was done in detail for approximately 60 commodity producing sectors in the system. The freight flows were developed in modal detail in terms of value tonne Ion miles zincl tr~n~n~rt r=`rm~l`~e ^~1 -~:1~ .. national totals for each mode. _7 ~_7 ~-~ ~-V ~11~3 ~1 ~l~Qll~ll~U W1~11 In view of this experience, and with the availability of freight flow data from the 1 993 Commodity Flow Survey (CFS), it is feasible to construct an update of this system, probably with detail for each ofthe 172 BEA areas. The input-output matrices for each area would be developed from the latest data from the econorn~c censuses with imputations from the latest national I-O table as necessary. Assuming the CFS data will be available on a five-year cycle in tandem with the economic censuses 20It is obvious that this system defines economic development patterns in a static sense; however. implicit ^ec~ll~r~;~e who ~^r~ ~ ~.,~1~~~ ~ 1~ ~ ~ ~ · ~ . "~lI~LlVlIO "UV"L ~V11~1111~ "~V~lUp111~11L =~ Il1~ 01 Fly Ores or nelgnt transpo~atlon needs--this system makes the assumptions explicit, identifiable and more amenable for direct adjustment based on current information and insights. 2iJack Fauceu Associates, "The MuRiregional Input-Output Accounts, 1977," described in 6 volumes (NTIS PB 83-258525) with data tapes available Mom the National Archives (3-235-84-0011. NCHRP-Multimodal Transportation 83 Project 8-32~5) Planning Data

.JackFaucettAssoci~es. Inc. Final Report March 1997 taken by the U.S. Bureau of the Census, along with national input-output tables, most of the data needs for updating the system on a routine basis each five years would be assured. Current Developments. The Bureau of Transportation Statistics, (BTS) U.S. Department of Transportation is currently developing a National Transportation Atlas Database (NTAD) within the framework of a national GIS-based transportation network. The Oak Ridge National Laboratory (ORNL) is assisting in the development of the database; progress on the development is described in a current Oak Ridge National Laboratory paper.22 Currently, the database includes a GIS-mapping system of the highway, rail, air, waterway, and pipeline networks, covering most of the strategically important components ofthe proposed National Transportation System. Intermodal facility locations are to be added by early 1996. Plans include the addition ofperformance measures for the networks and demographic and economic data for principal nodes. When completed this database will provide an excellent network for analyzing and forecasting interregional freight movements in commodity and modal detail by principal transportation corridors with origin-destination detail at the NTARS23 level. If the NTAD were currently implemented it would provide a large part of the information required to develop the Intermodal Management Systems for each state, assuring consistency among the networks and traffic volumes in each state (e.g., an analysis ofthe 1977 Commodity Flow Survey data for Pennsylvania shows that approximately 58 percent of the value of freight shipments originating in the state were destined to other states, and 54 percent of shipments terminating in the state originated in other states). (An un-estimated percent of total traffic in the state represented by traffic passing through the state from other state origins and destinations would also be provided by the NTAD.) Other regional database and network analysis proposed for development in the near future would benefit extensively from the NTAD. These include, for example, the BiNational Border Transportation Planning and Programming Process study dealing with increased freight flows between the U.S. and Mexico projected under NAFTA, and the associated infrastructure requirements, undertaken by Barton Aschman Associates, Inc., and coordinated by the Arizona Department of Transportation. Another example is the current Western Transportation Trade Network (WTTN) Study covering 14 western states which is to provide an inventory of transportation networks, and intermodal transfer facilities, serving these states and the connectivity ZZ"A National Transportation Network Analysis Capability: Issues arid Challenges," by Frank Southworth, Center for Transportation Analysis, Oak Ridge National Laboratory, drain prepared for The National Transportation Network Analysis Workshop, Sept. 6-7, 1995, Crystal City' Virginia. 23National Transportation Analysis Regions representing aggregations of the 172 Business Economic Areas (BEA) into 89 NTARS. The BEA definitions were revised to total 172 in early 1995 by the Regional Economics Analysis Division, U.S. Department of Commerce. NCHRP-Mu~itnodalTransportation 84 Project8-32(5) Planning Data

Jac* Faucett Associates, Inc. Final Report March 1997 - with state networks bordering the WTTN region. A study on the Southwest Passage Corridor just started by Jack Faucett Associates for the Southern Califomia Association of Goverrunent will provide another regional database on commodity flows through the Southwest states together with NAFTA trade and trade through the southern California ports. The national and inte~Tegional freight flow and forecasting system, based on an interTegional input- output model that would tie freight flows to economic production and consumption in each region, as described earlier, could provide a consistent set of forecasted traffic to be loaded on the NTAD transportation network with O-D pairs at the NTAR24 level and, probably at the 183 BEA level (assuming the 1993 CFS flows would be imputed down from the 89 NTARS O-D flows and balanced with production/consumption at the BEA level). This points up the potential for eliminating duplication of efforts, and the cost-effectiveness of a nationally coordinated interTegiona1 freight flow and network system for analyzing current efficiency and performance, and for projecting infias~ucture needs. In addition to providing guidance to state plarlIiing agencies, the system would provide MPO's with forecasts of incoming arid outgoing freight by principal corridors, serving Weir general areas, which must be distributed arid collected, respectively' in their planning areas. Existing Databases for Interregional Freight Transportation Planning. Principal existing comprehensive databases on interregional commodity flow are the 1993 Commodity Flow Survey (available at the NTAR level In 1997), the Waterborne Commerce Statistics, the One Percent Rail Waybill Sample, the Census Foreign Trade Statistics, the Port Import Export Reporting System (PIERS), arid Trar~search (Reebie Associates). These and partial databases on commodity flow and related databases useful for interregional freight transportation planning are documented in the separate Compendium of data sources as part of this study. Urhatc Freight-Planning for urbar~ freight movements received attention in the late 1960's and 1970's, but this activity waned until the passage of the ISTEA in 1991. An excellent report by the Texas Transportation Instituted documents the earlier activities and provides guidelines for urban traffic planners for collecting data arid forecasting urban freight movements. Several points of interest made in the 1979 study are: 1. trucks generally constitute about 1 5 percent of urban non-freeway traffic; 24National Transportation Analysis Regions representing aggregations of the 172 Business Economic Areas (BEA) into 89 NTARS. ~sFederal Highway Administration, U.S. Department of Transportation, Urban Transportation Planning for Goods and Services: A Reference Guide June 1979. NCHRP Multimodal Transportation 85 Planning Data Project 8-32~5)

Jack Faucett Associates. Inc. Final Report Marelt 1997 2. about 10-20 percent of urban truck trips are made by service trucks-the remainder represents trips for pickup and delivery of goods; 3. peak volume for truck hips occurs between morning and evening rush hours for commuting vehicles; 4. recommendations for reducing congestion from truck traffic include the provision for off street loading and unloading facilities, terminals for consolidating freight deliveries and pickup trips, land use restrictions on activities that generate large freight volumes (restricted to locations with ample street and ramp access), and development oftruck-only roads/ramps. The following alternative forecasting methods were suggested in the study: 1) truck percentage; 2) truck generation rates; and 3) systems modeling. These methods are described in Exhibit 21, along with their data needs and advantages and disadvantages (reproduced from the 1 979 study). A recent study sponsored by the Planning Section of the Metropolitan Transportation Commission, Oakland, California is an excellent reference on the current status of planning with respect to urban goods movements.26 It is based on a review and analysis of recent trucking survey results and points up the fact that development of detailed studies of truck travel in urban transportation planning is the exception rather than commonplace. The detailed findings of this study are presented in Appendix 4. A few of the salient findings were as follows: . . few urban areas in the country have extensive experience in conducting truck surveys and truck travel demand forecasting; most MPOs or regional transportation planning agencies continue to generate their truck trip estimates based on O-D surveys conducted in the 1960s and 1970s; the most common uses of truck data are for regional truck travel model development and corridor/route analysis; the most common survey method for constructing truck travel surveys in urban areas is the combined telephone-mail out-mail back method which is also the most cost-effective method; the most common source for drawing the survey sample is the Department of Motor Vehicles registration files; 26Samuel Lau, "Truck Travel Suverys: A Review of the Literature and State-of-the-Art,n MTC, Oakland, CA, January 1995. NCHRP Multimodal Transportation 86 Planning Data Project 8-32~5)

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Jack Faucett Associates' inc. Final Report . March 1997 most truck trips serve local regional needs-through hips are usually less than 10 percent of total trips; and truck travel during peak periods ranges from less than 9 percent to as high as 17 percent of total vehicle trips during peak hours. Recent Developments. A comparison of the salient findings from these two studies in 1979 and 1 995, respectively points up the fact Mat only recently has much attention been given to urban freight planning. Several points of interest from the two studies follow: apparently, urban Duck trips have not changed much as a percentage of total vehicle trips-- averaging about 1 0 to 1 5 percent on non-heeway traffic; 2. more effort is now made in collecting O-D data on truck trips; 3. modeling of urban traffic generally has aggregated Duck vehicles with passenger vehicles; and 4. the recent emphasis on intennodalism has stimulated increased attention to the analysis of urban freight transportation. Inte~modal transportation has existed for a long time between the surface and water and air modes. The rapid increase in containerization over Me past several decades improved the efficiency of this multi-modal transport; it also pointed up the efficiency of ~uck/rail intermodaTism which was recognized as a policy issue in ISTEA. Previously, this issue was largely ignored in private planning by rail and Ducking firms which fostered aggressive competition between the two modes rather than cooperation for efficiency improvement. Since interrnodalism affects long-distance transport, it may be viewed casually as not having much effect on urban freight trarlsportation planning. However, this is not the case since most freight originates or terminates in urban areas and intermodal connections are generally close-by urban areas. The location of these intelmodal terminals, arid access, must be factored into urban traffic planning. A recent study by the Transportation Center, University of Tennessee27 is a very intensive study of urban freight systems based on major surveys of freight systems in the Metropolitan New York area and in Vancouver, British Columbia, arid selective data from other surveys in 15-20 urban areas around the United States. Emphasis was placed on rail-truck terminals, port operations and 27Published by the U.S. Department of Transportation, Federal Highway Administration, Characteristics of Urban Freight Systems (CUFF, December 1995. NCHRP Multimodal Transportation 88 Planning Data Project 8-32(5)

Jack Fauceft Associates, Inc. Final Report , ,Uarch 1997 intermodal connections for import-export trade and local urban truck distribution. Some generalizations on the characteristics of urban freight systems were made by the authors ofthe study, but they were reluctant to make general comparisons due to the different logistical scenarios found among the metro areas. Current Data Collected and Collection Methods. Data collected by large MPO's are illustrated in Exhibit 22, reproduced from the Oakland MTC study. There is lime evidence that much data are being collected by the smaller MPO's. Data Needs and Recommended Collection Methods. It is not evident that it is cost-effective to collect sufficient data to estimate the universe of urban truck movements. A pragmatic approach is to target movements that contribute significantly to congestion at ramps, intersections and on-street loading and unloading zones. Information on trip purpose, O-D and time of travel would be used to evaluate potential traffic control measures (e.g., truck routing, delivery hours, land use zoning) and to forecast growth in Suck tragic and congestion problems. The first step would be to identify these highly congested spots by observation. Video equipment would be employed to record license numbers on medium size and heavy Sucks. Registration records would identify owners, and analysis of these records would identify concentration of vehicle owners (carriers and private fleets). Fleets would be identified for survey at their base of operations. Fleets by declining size would be selected for survey up to the point that a major part of the truck traffic was covered among the traffic sites of concerns. A sample would then be identified to represent the remaining traffic among the congested sites of concern. Truck traffic originating outside the area could be surveyed by cordon surveys involving roadside interviews. However, it is suggested that survey information could be collected by the state D.O.T. at weighing and safely inspection stations, sorted out and distributed to the appropriate MPO . · - plannlng agencies. MPO's with nearby seaports could access the PIERS database to identify the commodity detail and O-D of shipments through the port; or, they could survey the trucking firms serving the port. A potential source of data on port shipments is the increasing use of electronic data information (EFi) for documenting shipments by shippers and carriers. The Foreign Trade Division ofthe U.S. Bureau of the Census has been using EFI records in compiling import statistics and plans to extend its application in processing export data.28 Presumably, this will provide inland O-D data not previously available from Census. The suggested strategy for data collection outlined above includes the following steps: 1. identify the problem sites for which data are needed, Transportation Research Board, Data for Decisions, Special Report 234, Appendix B. 1992. NCHRP-Multimodal Transportation 89 Planning Data Project 8-32~5)

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Jack Faucet; Associates, Inc. Final Report 2. identify the operations that generate this freight, and survey these operations to obtain the data needed. March 1997 This strategy is targeted to avoid roadside interviews that interfere with traffic flow and it should elicit more comprehensive information since it would take place in a more relaxed office environment. 2.2.2 Proposed Data Program and Organization Framework The "3-C" planning process (i.e., comprehensive, cooperative, and continuing planning) was successful in guiding transportation planning during the highway expansion era, but failed to implement coordinated planning across modes and~eva~ious levels of government. Consequently, current data programs and organization Dameworks have been developed by transportation planners in reaction to disparate planning analyses that vary why modal issues and across planning agencies. This reactive approach has resulted in data programs Nat are plagued by problems associated with data coordination, consistency, arid redundancy. The "3-C" process in plarlIiing was established in the Federal Aid Highway Act of 1 962. The Act required that grants of Federal Finds to urbanized areas over 50,000 population be conditioned upon a formal system of urban transportation plar~rung with links to land development. The Secretary of Commerce was also directed to cooperate with the states t'...in the development of long-range highway plans and programs...properly coordinated with plans for improvements in other Affected forms of transportation and which are formulated with due consideration to their probable elect on the future development of the urban area... " In spite of this legislation, coordinated planning across transportation modes and regional jurisdictions was slow in developing (e.g., by 1972 only New York and New Jersey had established state departments of transportation, although at least six other states were in the process of doing so).29 Perhaps this was an unfortunate result of a missing "C" in the "3-C" process: that corresponding to coordination. However, coordination in urban transportation planning was advanced by the creation ofthe Urbar~ Mass Transportation Administration in 1968, and advances in multimodalism have recognized the need for coordinated transportation planning. This need stems partly from the changing planning focus that arose from concerns about the contribution of transportation to energy shortages arid air pollution. These concerns highlighted the problems associated with transportation solutions to efficiency problems that promoted system expansion. The 29George M. Smeck, The Urban Transportaiion Problem A Policy Vacuum, chapter in Urban Transportation Problems: New Perspectives, David R. Miller, Editor, Lexington, MA, 1972. NCHRP- Multimodal Tra~sportafion Planning Data Project 8-32(5)

Jack Faucet' Associates, Inc. Final Report stress of imnrovin~r ~v~t~m t~ffir.ir>.nr.v ~ith^~lt Gerry;; she ~: ~ _¢ _.1 · Marc* 1997 _ ____ ~ _J ___^,, ~ .._ ,~4~v~ ~1141~111~ ~1~ Ill ~1 omer societal gods has diverted the future course of transportation planning from an isolated, regional process to a coordinated, integrated process. The underlying objective of this study is to develop a Data Program that embodies a coordinated approach to assessing data needs, developing data organization frameworks, collecting data, and disseminating data among all data users. Task 1 has focused on a developing a strategic framework for assessing data needs (i.e., the Business Model). The first part of Task 2 has focused on describing current data organization frameworks and evaluating the need for new frameworks for a comprehensive, coordinated, and integrated multimodal transportation planning focus. The purpose of this sub-section is to propose a new data organization framework that transcends the planning missions, objectives, strategies, and data needs of transportation planning agencies. The development of a new data organization framework, however, cannot be conducted in isolation, since data organization simply should be only one component of a coordinated, comprehensive, consistent, and integrated Data Program. As a result, a conceptual definition of the proposed Data Program was necessary. This definition is provided below. Proposed Data Program The most important element of the Proposed Data Program invnl`~Pc the development of a Data Task Force to ensure coordination across all user groups within a state. Given the state's role in resource allocation and its place in the hierarchy of geographic coverage, the development of the Data Task Force should be led by the state-DOT, as depicted in Exhibit 23. Other members of the Data Task Force should include representatives from state-DOT functional offices (e.g., operations), MPOs, other transportation data user groups such as air quality planning agencies, and other public and private stakeholders. The mission of the Data Task Force can be articulated as follows: To ensure coordination and collaboration in the assessment of data needs and in the organization, collection, and dissemination of data across all user groups within the state. Each of the elements of this mission (i.e., needs, organization, collection, and dissemination) comprises a component of the proposed Data Program, as depicted in Exhibit 24, and reflects an objective of this research. Specifically, the Data Needs component involves the application of a strategic planning platform to assess data needs associated with multimodal planning. The Business Mode! was developed and employed in Section 2.1 to strategically assess data needs of planning agencies. Once data needs have been assessed, the organization of data must be considered. The Data Organization component of the proposed Data Program must be comprehensive in characterizing the transportation system. Data organization frameworks are the subject of Task 2, and a proposed framework is described later in this sub-section. NCHRP Multimodal Transportation 92 Planning Data Project 8-32~5)

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Exhibit 24 Proposed Data Program COORDINATION Integration l 1 1 1 Data Task Force ~ . .. ~ , Data Needs Assessment Data Organization Data Collection l Data Dissemination 1 r Business Mode! System Attributes  Primarv Sources Secondary Sources Storage & Access , Consistency Organization 1 l

Jack Faucea Associates Iliac. Final Report . ~_ The Data Collection component of the proposed Data Program delineates primary and secondary data collection activities that must be undertaken by planIiing agencies to support plarlIiing activities. A primary objective of the Data Task Force will be to assign data collection responsibilities across planning agencies perhaps based on economies of collection, geographic scope, resources, and data uses. Data collection activities should parallel elements of the Data Organization framework. The Data Collection component ofthe proposed Data Program is also addressed by Task 4 ofthis study since that task requires the development of a Compendium of data collection practices to identify innovative techniques that enhance the planning process, and by Task 5 of this study where the economics of data collection activities is addressed. The Compendium is bound in a separate volume. March 1997 The final element ofthe proposed Data Program involves Data Dissemination. Data dissemination addresses the need for data integration arid available technologies for data storage arid sharing. This component is addressed by Task 6 of this study. The Data Organization component of the proposed Data Program is described below. Proposed Data Organization Framework - Data organization is fimdamental to the successful performance of transportation plarming. ~. . . . . . Given the large quantity of data necessary for the development, evaluation, arid Implementation of transportation strategies that support planning objectives, the manner in which data are grouped impacts the efficiency and stability of the planning process. Stability is impacted because the time arid cost of collecting data, as well as the need for systematic and reliable monitoring over time, work against constant modification of data bases. Efficiency is impacted because practitioners rely on timely access to information in the development of plans and projects. Stability and efficiency needs suggest that transportation data may be best structured not by planning issues, which tend to vary spatially arid temporally, but by the major attributes of the transportation system which remain relatively constant and facilitate the data retrieval process.30 A suggested hierarchy for the data organization framework has been established along the following: I. major data components, 2. data elements within each major component, 3. data sets within each data element, and 30The data organization framework proposed in this study draws on consents fir3:t H~nvPH by the Tranepn - align Research Board's Special Report 234: Datafor Decisions, 1992, which presents the National Transportation Performance Monitoring System (NTPMS). NCNRP-Multimodal Transportation Planning Data r ~. ~ ~ v ~ GUAVA A 95 Project 8-32(5)

.7ackFalccett Associates, inc. Final Resort March 1997 ~_ 4. data items within each data set. A more robust version of the proposed data organization framework has been developed (see Appendix 2). Specific data sets that can be grouped under each of the data elements of the data organization framework have been identified. although effort has been exnen.1~.11 to irl~ntif~r the ~1 ~ In: ~ ~'1 ~~ ~_ _ 1 _ _ _ _1_ _ 1 _ ___ _ . .1 , · , ~} ~ I've t p~c ua~a ~;~ under In elernen~, me emlre spectrum of data needs en cl nl~nnina its in probably not represented. ~it _ ~ Data components. As discussed in Section 2.1, multimodal transportation planning can be defined by the supply and demand attributes of the transportation system. Together, the supply and demand components of the proposed data organization framework provide information on the quantity of travel by mode along the various facilities of the transportation system. Furthermore, the interaction of supply and demand at any given moment in time defines system performance, and externalities associated with travel define the resulting impacts of this interaction. As a result, the four major attributes of the transportation system are supply, demand, performance, and system impacts. Therefore, to ensure stability and efficiency in the planning process, transportation data should be organized along these broad data components, as depicted in Exhibit 25. Data elements. Illustrative data elements under each of the major components are also listed in Exhibit 25. The supply data component of the proposed framework contains data elements -describing the physical make-up of facilities and their condition, as well as data elements describing the characteristics and financial condition ofthe major transportation service providers. The demand side contains data elements that focus on the transportation needs of passengers and freight, the quantity of travel, the spatial and temporal distribution of travel, and the behavioral characteristics of system users. The performance and impacts components of the proposed data organization framework contain data elements that describe how well the system meets the travel needs of citizens and commerce and at what "cost" as defined by the impacts of transportation. The impacts data component of the Damework depicted in Exhibit 25 is comprised of data elements that contain information on the effects of transportation on other societal goals including: air quality and other environmental goals, land use, energy conservation, and economic productivity and grown. Each of these external effects has an impact on both Me demand for arid supply of transportation services. For example, air quality concerns may impact the type of mode that a traveler chooses as well as the type of facility that is built. Data sets. Data sets are groups of data items describing the characteristics of specific data elements. For example, household characteristics represent a data set consisting of demographic, economic, and social data items that are usefill in modeling the travel arid trip generation of the household members. The data items may not be useful per se, but the relationship, for example, between household income and public transit trips, is useful to planners in estimating potential transit ridership. Thus, it is import that the data items be collected in a manner that ensures that different NCHRP Multimodal Transportation Planning Data 96 Project 8-32(5)

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Jack Faucett Associates, inc. Final Report March 1997 characteristics observed are from the same household, at the same time, and under a consistent set of definitions for each data item. For example, observations derived from an on-bonrd transit survey may not properly reflect other household characteristics of the rider important to trio veneration modeling. -r A ~^ Data items. As identified in the sample assessments of MPO and state-DOT data needs (see Exhibit 14 and 16 in Section 2.1.3), travel data include data items describing the number of trips by vehicle class, VMT by hour, VMT by time-of-day, bus ridership, rail ridership, freight ton-miles by mode, and other indicators of system usage. System perfonnance is measured by how well transportation satisfies the accessibility and mobility needs of citizens and commerce; the level of service and the quality of specific services; the satisfaction of accessibility and mobility needs of citizens in a safe manner; and the cost of using transportation services and facilities. Exhibit 14 (Section 2.1.3.1) provides examples of data items characterizing system performance (e.g., person hours of delay, peak period speed, average travel time' trip cost by trip purpose' etc.). Finally, sample data items corresponding to air quality data elements and sets under the impacts major component include vehicle registration distributions by vehicle type and vintage, mileage accumulation rates by vehicle type and vintage, VMT by functional road class, and other inputs to emission factor models (i.e., MOBILE and EMFAC). The Need for Standardization a'2d Data Co'2s~sfency-The organization of data along supply, demand, performance, and system impacts components is mainly a matter of convenience for referencing to data types that may be labeled under different names in different agencies or grouped in a larger or smaller number of classifications across agencies. Standardization of coverage across agencies of data elements within major data components is more of an issue in data exchange but is of secondary importance, especially since some agencies will not need to, or cannot afford to, cover all data elements listed under each component in Exhibit 25. Standardization of definitions and coverage across agencies becomes important in the development of data sets since they provide the data foundation for most ofthe analysis for transportation planing; data exchange and application in analyses (models) among multiple users is facilitated by this standardization. Standardization in definitions, coverage, and consistency becomes most important for data items within each data set; data items provide the basis for calculating relationships among the data items which are the data input to most analytical processes. Consistency among data items is an issue in primary data collections and in secondary data sources. A description of primary arid secondary data sources is provided below. 2.2.3 Inventory of Traditional and Non-Traditional Data Sources A brief description of the separate Compendium of data sources follows below. The separate report contains over two hundred pages. NCHRP - Multimodal Transportation 98 Project8-32~5J Planning Data

Jack Faucett Associates, inc. Final Report March 1997 ~1~:~ I stem ~ d.~16:~1~1 T ___:_ ~ ^1~ ~ ~ 11 w~Il`;luslons from tne 1vlulllmoua1 1rarlsporcallon planing L)ata-t research indicate that changes in transportation planning focus, due to the emphasis on multimodalism and the 1990 CAAA, have created the need for new data characterizing transportation supply, demand, impact, and performance. In addition, it was determined that the Business Model was a logical method for a transportation planner to assess data needs, either at the state or local level. The planning needs and requirements that are subsequently recognized may demand specific information needs, models, and data which are not currently available. Furthermore, the Data Program outlined under this research effort and presented in both the Guidance Manual and this Final Report describes primary and secondary data collection activities necessary to support the planning objectives and strategies of the various planning agencies. The aim of the Compendium is to organize an inventory of transportation data sources that can be utilized to supply the data identified through a strategic data needs assessment. For the purposes of clarity and efficiency of use, the report has been divided into four sections: Data Collection Methods Secondary Data Sources Technical Support Resources Internet Resources The sub-section on Data Collection Methods has been divided into two primary sections which detail methods and technologies related to l)sample surveys and 2)travel monitoring. These methodologies should assist state-DOTs and MPOs in gathering both conventional planning data and new data dictated by multimodal planning and the 1990 CAAA and TSTEA. Discussions of implementation techniques (e.g., phone, mail, video, etc.), although not addressed directly. are interwoven into many of the descriptions of primary data collection methods. , , . The Secondary Data Sources identified include those available from Federal agencies, such as the Bureau of the Census and the Bureau of Transportation Statistics (BTS)' state agencies, and private institutions currently involved in data collection and dissemination. This sub-section begins with examples of applications of secondary data that were not originally collected- for transportation purposes (e.g., tax data). As the cost and need for data collection increases, as well as the budget constraints at Me state and local level, there has become a heightening demand for identifying data that has already been collected by other agencies or private enterprises that can be adapted to transportation planning needs. This appears to be an area of increased interest and need for iilture research. 3iThis stand-alone appendix is part of a larger research effort conducted for the National Cooperative Highway Research Program (NCHRP) entitled Multimodal Transporatation Planning Data, project 8-32 (51. NCHRP Multimodal Transportation 99 Project8-32~5) Planning Data

Jack Fancett Associates, Inc. Final Report March 1997 The listing of traditional secondary sources have been divided between those providing freight data and those providing passenger related data. In order to ensure complete coverage of sources and ease-of-use ofthe Guidance Manual those sources that are 1l.~fil1 for both He ~ =~;~1~+ 1~..~ 1~ ~ ~ ~1 . . ~ 1: _ _ ~ _ 1 AIL _ 1~ 1 _ _ ~ _ _1 · 1 , 1 , ~ Uphill UUpil~U LO DO Included In Dom secllons. 1 he breakdown of sources within the Freight and Passenger sections follows the same format as the data organization framework (e.g., supply, demand, performance, system impacts, etc.) presented in this section. In addition, all sources have been referenced within the framework by an unique source number (found in the top corner of each source) to allow users to move quickly from identifying the type of data needed to where that data can be found. A majority of the secondary sources were identified through the BTS's Directorv of Transportation Data Sources. For consistency, the format for describing each source was patterned after the Directory and includes, where possible: . Mode Abstract Source of Data Attributes Significant Features/Limitations Sponsoring Organization Performing Organization Availability Contact for Additional Information This section also contains the most recent Product Catalog put together by the Bureau of Transportation Statistics. The Catalog provides a list and a brief description of the products and services available from the BTS, currently or in the near future. The announcement lists both electronic (e.g., CD-ROM) end primed products. Alarcemaioritv of nrnduct~ Cry ~nnlir~hiP fly The data needs of the transportation planner. ~-, ~ - - r ~ ~ I ~~ ~ ~ ~rr''~V'', '" '-'' The technical support resources describe some organizations that could assist planners in filtering through and understanding large amounts of data and sources, as well as possibly recommend analytical techniques or software which can be used to manipulate transportation data. The Internet resources provide addresses and descriptions of multiple transportation related sites on the Internet, in addition to e-mail account addresses. A majority ofthe Internet-related listings were taken end, where possible updated from the BTS'sInternet Starter Kit published in 1995. It should be noted that the rapid expansion of Internet sites and technologies makes it virtually impossible to have a current and comprehensive listing of transportation related Internet sources. However, many of the key sites provided will undoubtedly provide connections to many of these newer sites. NCHRP Multimodal Transportation Planning Data 100 Project 8-32(5)

Jack FaucettAssocicltes, luc. Final Report March 1997 . 2.3 Task 5: Economic Analysis of Transportation Data Collection In this study data needs for transportation planning have been grouped into four principal components: supply, demand, performance and system impacts. Supply data includes information on the physical networks and services provided by commercial modes and associated costs. Demand data includes information on needs for moving people and goods over specified distances and routes and the associated costs of these movements. Performance data includes measures of how well supply is fulfilling transport demands at affordable cost and with achievable efficiency. System impacts measures the external effects ofthe transport system on the physical and social environment. The economics of data collections involves the optimal allocation of scarce resources in meeting competing and complementary needs of data for transportation planning. Issues involve assessing the marginal costs of specific data elements and data sets against their marginal contribution to understanding and modeling the behavior of suppliers and users of the transport system. We especially need to know more about the reaction of suppliers and users to policy initiatives aimed at improving the economic efficiency of the system and for internalizing the costs of external impacts. Today we have too much data and too little analysis related to its usefulness and cost-effectiveness in supplying the information needed for transportation planning. It is easier (but perhaps more expensive) to collect lots of data with the intent that it can be used somehow than to analyze ---rid , carefully the salient needs and to design en1clem ways lo collect abut `o ~ u~;~c ~<;c;u~. requires assessing total needs rather than compartmental needs since there can be large economies in combining data requirements in data collection activities. Moreover, much of the available data are not tailored to specific needs and are often redundant. Having too much data not well organized and not directly useful makes it more difficult to identify the useful data and it wastes resources that could be allocated to improving more directly usable data. Hence, we need to develop more limited data sets that are more cost-effective in meeting the salient needs for transportation planning. Guidebooks and manuals would be more useful if they- identified cost-effective data collection approaches after an analytical evaluation rather than presenting a smorgasbord of unevaluated data sources. Historically, most of the transportation planning was done in terms of the separate modes: rail, water, air, pipelines and highway. The need for more coordination in planning across modes has gradually become recognized and is now recognized formally in terms of intermodal planning. The awareness of the benefits of higher living standards resulting from economic efficiency in transport has partly led to deregulation that permits greater competition among suppliers in some modes but requires more cooperation in multimodal planning. Multimodal planning has been applied more to intermodal passenger travel than to intermodal freight. Since the nation's bill for transport of freight is estimated at over 400 billion dollars (compared with over 700 billion dollars for passenger travel) there is a large potential payoff in improvement in freight transportation efficiency. (Of course, the NCHRP-Multimodal Transportation 101 Project 8-32(5) Planning Data

Jack Faucett Associates, inc. Final Report - March 1997 cost of time by passengers is not included--difficult to estimate; however, the loss in productivity due to inefficiencies in intermodal freight transport is also not included). Data for analyzing this potential, and what transportation improvements financed by the public sector can be effective, is a priority. This analysis can also point out changes in public regulation of private carriers that will result in more cooperation among private carriers and healthy competition. Very few studies have tried to quantify the benefit/costs of selected transportation strategies for either passenger or freight movements. A seminal report, directed by Dr. Dudley G. Anderson at the Stanford Research Institute and published by AASHTO in 1977, is one example of such a study that exhibited very laborious and painstaking effort at quantifying benefit/cost.'2 A less detailed study by Todd Litman of the Victoria Transport Policy Institute measured benefits and costs associated with public transit.33 An excellent treatise on the role of increasing transportation efficiency as a key input to advancing productivity and economic development is presented in a recent study by David Lewis of the Hickling Corporation.34 Of course the importance of such studies is associated with economic policy objectives. Thus, the planner places value on data sets that is most efficient in the analysis needed for the objectives decided upon. This suggests that planners may wish to set their sights on data needs for analyses beyond the immediate problems that require their attention. They may be best equipped to educate the decision makers on the value of dealing with more fundamental logistics problems. In any event, planners can best identify data sets that are most useful in analyzing the specific problems confronting them. Of course there is a tradeoff in the value of the best data and its collection costs relative to other data sets. These data sets have alternative values in analyses of supply, demand, performance, and environmental impacts. The values and cost tradeoffs differ across MPOs due to differences in the scenarios and data collection costs associated with immediate problems to be analyzed. 2.3.! Data Components A transportation planning organization, as with any other organization, must evaluate its internal resources and operational structure' over which it generally has control, to interface optimally with - 32A Manual on User Benefit Analysis of Highway and Bus Transit Improvements, AASHTO, 1977. 33Todd Litman, "Defirung and Quantifying Public Transit Benefits," Victoria Transport Policy Institute, Victoria, B.C., April 15, 1996. 34NCHRP Report 342, Primer on Transportation Productivity and Economic Development, Sept. 1991. NCHRP - Multimodal Transportation 102 Project 8-32(5) Planning Data

lack Faucett Associates, Ine. Final! Report March 1997 external environments over which it has little control. This element of control and its implications for data needs will be examined in terms of the four planning components of supply, demand, performance and system impacts. These four basic groups of data needs are of equal importance since they are all necessary and complementary in transportation planning. In this sense they are like the four legs of a chair, each of which is necessary. However, data that is generated within the organization in administrative records presents less of a nrnhlem then `1~ Of~.Df~r~tPA h`, ^~;^ and social forces external to the organization. 2.3.1.1 Supply _ ~ ~ =_~_^ ~_~ V] -~VllV1111~ The state DOT's and regional MPO's (and their constituent agencies) have control over the highway and road network, arguably the most important part of supply even in the intermodal context. This control, and the physical nature of supply (networks and facilities), have important implications for data needs. By-and-large data on the characteristics of the network needed for planning are generated internally and are largely engineering and inventory-type data, as contrasted with behavioral data on users that are needed for the analysis of demand, performance and system impacts. Thus, supply data are for the most part available from administrative and engineering sources within the organization. Of course these data need to be organized and well defined for use for planning; however, the data needs can be more clearly defined than, for example, behavioral data on users, and they can be tailored to the specific planning priority needs of each organization and its partners in planning (state DOT, regional MPO's and their constituent agencies and, to some extent, federal agencies). Data on supply for some non-highway modes (airports and inland water and seaports) are also generally under public control and data on these facilities are available from administrative and engineering records. Information on rail lines and terminals and pipelines is generally not under the control of the planning agencies but may be obtained either from publicly available sources or through planning partnerships between the plarlIiing organization and private companies. The principal point is that data on supply are generally available from engineering and administrative records either internally available to the planning organizations or available from private sources. The data needs are more clearly defined than are those associated with behavioral characteristics of users of the transportation system. These data are very important for planning purposes but generally no extensive data collection systems are necessary. This lends itself to central control and standardization, and data sharing, far beyond that of demand data from disparate collection systems subject to uncertainties in interpretation of behavioral relationships needed for guidance in planning for improvements in the perfonnance of the transport system. 2.3.~.2 Performance Performance measures reflect the interaction of supply and demand in providing efficient transport service. Desired service levels are to some extent subjective since tradeoffs exist among the service NCHRP Multimodal Transportation Planning Data 103 Project 8-32(5)

Jack FaucettAssoci~es, lac. FznalReport March 1997 attributes. For example, speed, or trip time, is not an unambiguous nonn since it may increase at the expense of accidents, cargo damage or damaging environmental emissions. However, achievable speed may be an unambiguous desirable measure since time is valuable in either people or cargo movements and the option to reduce trip time, if consistent with other objectives, is a benefit. Performance measures should be a high priority and, given the current relative lack of emphasis on these measures, may be the most cost-effective area for data and information improvement. This statement applies not as much to direct observations on congestion and other trouble spots but to measures of the effects on performance of specific projects aimed at improvements in system performance. Projects includes, for example, TSM, TOM, TDM and additions and modifications to the system. Performance measures should have a high priority for planners since they can provide information on which parts of the multimodal (intermodal) system are functioning efficiently and which parts need improvement. This information is vital in directing marginal resources to applications that we have a high payoff ("a chain is as strong as its weakest linked. Overall system measures are important but the most cost-effective information is that which identifies the weak links in the system, provided of course that these weak links can be corrected at acceptable cost. Performance measures may also be used to evaluate the efficacy of specific projects aimed at improving the overall system performance. These projects include, for examples, the construction or designation of HOV lanes (add-a-lane or take-a-lane), ramp metering, automated signaling control, employer trip reduction plan (ETRP), staggered work hours, congestion pricing, 24-hour access for cargo deliveries at ports, electronic data exchange (EDI) to facilitate cargo intermodal transfer, automated traveler information systems (ATT S) and many other measures oriented toward optimizing the use of the existing transport network. These projects also include additions to the network such as urban by-pass routes and connectors between major arterials. The objective is to identify the most cost-effective measures for alleviating bottlenecks, and reducing delay times, by measuring performance before and after specific projects are initiated. Information on specific project effects on performance is an area in which information sharing can be very cost-effective. Specific traffic conditions and the effects of corrective action are not necessarily unique to specific locations, and the information gained can be relevant to planners in general in malting decisions on corrective projects. Of course dissemination is now made to some extent through journal articles, professional organizations and other media. Perhaps the establishment of a "clearinghouse" for classifying and disseminating these results through electronic media would have a large payo~in better selection of projects by virtue of reducing redundancy in the collection and evaluation of performance measures and thereby making more extensive and intensive measures affordable collectively. System performance is only partly under the control of the planning organizations which cart affect supply but which have only limited influence over demand which impacts on performance. NCHRP MuldmodFalTransportation 104 Project8-32~5) Planning Data

Jack Faucett Associates, sac. Final Report starch 1997 However, the measurement of performance is largely an internal responsibility, and relatively straightforward once the appropriate measures are defined. Data collections needed are locale- specific--current trouble spots and evaluation of specific projects aimed toward performance improvement. These measures are very important and should have a high priority. It is expected that a forthcoming study35 will address overlaps in measures, and identify general priorities in measures, but the general benefit/cost of these measures is not in question. 2.3.1.3-System Impacts Consideration of the impacts of the transport system on the rest of the economic system, on the environment and on the quality of life need to be an integral part of transportation planning. This has long been recognized since transport has always affected and interacted with all aspects of society, including economic development, economic and social mobility, the environment and land use. However, the primary goals of mobility and economic development dominated planning in earlier stages of transport development and the effects on the environment and land use received less attention. This started to change as long as 50 or more years ago when sociologists started to question the impacts on neighborhoods and the quality of life. This attention was elevated dramatically in the early 1970's with the advent of the so-called "energy crisis." The economic consequences of energy shortages became apparent and conservation of energy became a primary goal (witness "Project Independence.") This increased the interest in intermodalism as one path to energy conservation. Energy conservation was also promoted for its concomitant effect on reduction of emissions harmful to air quality which was beginning to receive major attention. However, a credible conclusion is that economic considerations rather then environmental concerns was the driving force behind energy conservation which in turn was a factor in promoting intermodalism (multimodalism) as an integral part of transport planning. The attention to multimodalism in transport planning culminated in the early 1990's in the passage of OAAA ~n(1 T~TF.A wrhirh mandated its integration into transport planning. System impacts, referred to as externalities, do not generally lend themselves to direct observation and quantification. For example, impacts on air quality are generally inferred from models with technical coefficients derived in laboratory simulations of engine emissions under selected operating conditions (reflecting operator behavior, climatic conditions, load characteristics and engine vintage). Emerging technology holds promise for collecting emissions data under actual driving conditions by roadside collecting ins~nents. In the meantime most of the information on emissions is inferred from data collected on the demand attributes of passenger and freight movements through applying models that incorporate technical coefficients from laboratory simulations. 1 ~O ~_ 35NCHRPProject8-32~2),MultimodalTransportation: Development of a Performance-Based Planning Process. NCHRP Multimodal Transportation Planning Data 105 Project 8-32(5)

Jach-FaucettA3sociates, 1nc. FinalReport ' March 1997 The relationship between transportation and land use is a two-way street: existing land use patterns given rise to transportation needs and transportation development to meet these needs has a feedback on land use patterns (witness the density effects on business and residential locations of new transit stations and beltway hi~hwavsi. The feerlhnok effe.~.t in Grit VPt ``lP11 1l"~l~ret~r`rl ~ ;= A;~:~..1` `~ ~^~ ^~ ~ ~ ~ ~V ~ _AA ~1~ ~ EVER ~11~ 1O Lull al L tO model. The same may be said in general for economic development benefits firOm tr~n~n~rt~tinn development. rid A conclusion that may be drawn from the above discussion is that data needs for measuring Item ; ~ A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ _ _ 1 1 or ~ _ _ ~ _ ~ _ _ 1 ~ 1 ~ ~ ~ 1111~ ILLS ~,cllcrmly clr~l~r o1 ~ recnmca1 nature temlsslons), are available from administrative records (land use), can be drawn from data collected for analysis of demand (population), or are available from secondary sources (economic development data). Measurement and forecasts ofthese effects depends largely on analysis of data available from other activities or from laboratory testing. In summary, they do not entail large new data collections on a scale comparable to the (lilts needs ¢~ _ ~ _~ _ ~ . _ _ 1 _ ~ 1_ _ ~ _ ~ _ 1 ~ 1Ut U~lilallU LIIlaly:ilti U1 U1~ Utile ll~tUi for performance measures and project evaluation. This is another information area that benefits from cost sharing of research that has application beyond the local area of focus in the research. 2.3.1.4 Demand Demand by users of the transportation system is determined generally by forces exogenous to the system but responds to system supply and performance. It can be influenced by transportation control measures, transportation demand management and transportation supply management (TOM, TDM and TSM). Demand is derived from the demand for mobility of passengers and goods; this demand in turn is driven by demographic and economic factors and technological advance. It is first necessary to understand the interactions among these drivers of transportation demand in order to understand how to improve the performance of the transportation system. It is also necessary to understand how the users of the system will react to changes in the system that are aimed at improving the performance of the system. _ ~· ~ The complexity of analyzing and modeling the behavioral attributes of users of the system have led to the collection of vast amounts of data. It is these data collections that offer the major opportunity for improvement in the efficiency of collection, organization and data sharing among the transportation planning organizations. Consequently, we will focus on this opportunity in the sections that follow. 2.3.2 ~OSI-~IIeCTlVeneSS OI Data ~ollecuon for 1 ranspo~ation Demand Analysis As discussed above, data on supply, demand, performance and system impacts are all needed--and it was suggested that data on performance should have a high priority. However, most of the data needed on supply, performance and system impacts are available internally, can be collected in a NCHRP-Multimodal Transportation 106 Project8-32(5J Planning Data

Jack- Faucet Associates, Inc. Final Report March 1997 fairly straightforward manner or shared among planning organizations.36 The needs for these data are also more flexible across planning organizations, with different priorities, depending on local traffic conditions, the configurations of the network and land use patterns. On the other hand, data on demand is largely external to the planning organization and depends upon the external environment, subject to more uncertainty and naturally not under the control of the organization nor as well understood as data and information largely internal to the planning organization. Obviously, to a large extent, we do not know exactly what data are needed or work most efficiently in modeling and forecasting demand. Since demand originates in many disparate sources, and depends on behavioral attributes and economic forces, subtle cause and effect relationships are more difficult to quantify than relationships from engineering and other technical data. Hence, it is no wonder that modeling demand is less advanced than would be desired for purposes of designing efficient data collection systems. As modeling demand becomes more perfected In the future, it will become clearer as to which data are more essential through feedback from the modeling results. Thus, in time, we will be able to assess the cost-effectiveness, if not the ultimate benefit, of specific data sets and data items. In the meantime, we must strive to narrow our "needs", and accord priorities, based on our experience to date, and to evaluate the most cost-effective methods of collecting the data identified as essential to demand analysis. In the sections that follow a modest attempt is made to describe a process for evaluating the cost- effectiver~ess of specific data sets and data items for demand analysis.37 Appoint a small data committee (not more than 2-3 persons). The committee will identify data sets currently used in short-tenn and long-term planning functions, as well as data not now available but deemed essential, by planning function or purpose, e.g., congestion management in specific corridors, evaluation of potential effectiveness of projects in the TIP, longer term forecasting of network loadings by corridor, etc. The committee will request top planners to assign values to planning purposes based on a scale of 0 to ~ 0, ~ O representing the highest priority. 36In the case of performance evaluation and systems impacts, information on analytical results, rather than raw data, provides the major opportunity for data sharing. 7This process is a simplistic version of the Analytic Hierarchy Process in which attributes are paired and ranked on a similar scale and checked for interconsistency. See Thomas L. Saat:y, "A Scaling Method for Priorities in Hierarchical Structures," Journal of Mathematical Psychology 1 5 (June 1977) pp. 234-8 1; and Thomas L. Saaty, The Analytic Hierarchy Process (New York: McGraw-Hill, 1980), pp. 17-21, 165-192. NCHRP Multimodal Transportation 107 Project 8-32¢5) Planning Data

Jack Faucett Associates, Inc. Final Report A. Request the planners/users of the data sets to assign a value to each data set for each planning purpose on a scale of 0 to 10. These assignments would be made based on the explanatory power of the data set in the models used by the planner/user, or by his/her judgment as appropriate. Multiply the values assigned for each data set under each planning purpose by the value assigned for the nIar~nin~ nurr,~se under which it is needed. - rid o ~-~r~ 4. Develop an unique list of data sets identified for any and all planning purposes along with the values assigned under each planning purpose as calculated in step 3 above. Add the values assigned to each data set under each planning function to derive an overall value for each data set. Review these final values with the top planners and adjust the values if appropriate by judgment. March 1997 These four steps will provide a rough value ranking for each data set. At this point the efficiency of the alternative collection systems that might be used to collect the data should be evaluated in conjunction with time priorities for the data sets. This could lead to resource savings as well as provide a measure of cost effectiveness of collecting the data sets. The suggested steps for calculating these cost-effective measures in view of time priorities are described below. SStrategic Evaluation of Collection Methods There are a number of tradeoffs that must be evaluated when calculating the most cost-effective ways to collect data. There are economies of scale savings to be realized if all of the data assigned relatively high values can be afforded within the budget for data collection and associated data processing. As a general rule of thumb, the greater the number of data sets that can be collected from a single source, the smaller the collection cost per set. In addition, collecting as many data sets as possible from one source insures the integrity of relationships calculated from the data, e.g., autos owned by income group. These advantages favor collecting data at the source that generates the transportation activity, i.e., households and shipper and trucking firms. On the other hand, time priorities and budget constraints may dictate other collection methods. For example, an immediate problem of congestion in a given corridor may require priority attention. Or, budget constraints may only permit collecting data relevant to NCHRP Multimodal Transportafion 108 Project8-32~5) Planning Data

Jack FaucettAssoc~tes, Inc. FinalReport Starch 19¢7 . specific high density corridors rather than data on traffic for the total planning area. In these events it may be more cost-effective to collect data targeted only on these corridors through license plate identification and mail-out-mail-back surveys, roadside interviews, or parking lot surveys. Smaller samples targeted to these corridors would suffice as contrasted with household surveys that would require larger samples to pick up observations relevant to these -specific corridors, and costs would be less. . · ~. . These tradeoffs impose a need for flexibility in the data collection systems and an awareness of the alternative costs among the various collection methods. 6. Alternative Collection Costs In order to be in a position to minimize costs over, say, a five-year data collection plan, and stay within budget each year, several steps are necessary: a. b. Identify the data priorities by year. Identifier the various collection methods feasible for collecting the priority data needs as identified by planning year. Examples household survey employment survey shipper or trucking firm survey roadside or parking lot interviews license plate identification and mail-out-mail-back survey other methods Cost out these alternative methods for collecting the data sets programmed for each year. The costs will vary by sample size required in each collection method depending on the size of the area targeted by the data need, response rates and number of data sets covered by each collection method. Estimates of costs may be based on local experience or imputed Tom similar experience in other planning organizations. The following sources are a few examples that provide estimates of survey sample sizes needed and costs. This is just a small sample. There are marry studies by MPOs dealing with their specific areas, especially on congestion problems, which we . . ~ NCHRP Multimodal Transportation 109 Project 8-32(5) Planning Data

Jack Faucett~sociates, Inc. Final Repo't Starch 1997 have reviewed--and we would be glad to supply references to, or abstracts of, these studies upon request. Also there is an invaluable annotated bibliography on Innovative Practices for Multimodal Planning for Freight and Passengers (NCHRP 8-32~) recently available from the Transportation Research Board. Travel Survey Manual, Cambridge Systematics, Inc., for the Federal Highway Administration (draft). Short Term Travel Model Improvements, U.S. Department of Transportation, Oct. 1994. Traffic Detection Technologies, Federal Highway Administration (Draft May I, 19961. Samuel W. Lau, Truck Travel Surveys: A Review of the Literature and State-of-the-Art, Metropolitan Transportation Commission, Oakland, CA, January 1995. Finally, select the combination of data collection methods that will provide data sets programmed for collection in each year at the least cost. d. 7. Review and Reevaluate Priorities Sum the costs estimated for the five-year period. Examine the effect on this total cost of shifting priorities among the years. Shifting the priorities may allow economies of scale--by collecting a broader scope of data from each source. For example, if a comprehensive data base of travel throughout the planning area will be needed during the five years for long term forecasting purposes, switching this activity to the first year may obviate the collection of fragmented needs over the five year planning horizon. If rearrangement of priorities over the five-year period affords significant cost savings, then it becomes a matter of judgment as to whether switching priorities is feasible. Technical, administrative, or political considerations may make it infeasible. However, at least the approximate cost of maintaining the original schedule will be known and taken into consideration as appropriate. 8. Calculate Cost-Effectiveness Measures and Reevaluate Data Priorities After the final ordering of priorities for collection of data sets in step 7 above, costs per data set can be calculated. This would be done by dividing the estunated cost of each data collection system employed by the number of data sets collected and assigning He average cost so calculated to each data set. The cost-effectiveness measure for each NCHRP - Multimodal Transportation 110 Project 8-32~5) Planning Data

Jack Faucett A~'soc~ate£, Inc. spinal Report _ ~ _ data set would then be calculated as the cost in relation to its effectiveness or value, as previously assigned back in step 4 of the initial analysis which reflects the value assigned by the planner/modelers who are users of the data. This of course is not a benef~t-cost measure but a simple measure of the relative "bang for the buck" based on the usefulness of each data set as assessed by the practitioners. (Note: since there are fixed costs in any data collection - system, when more than one data set is collected from a collection source and some highly valued items are included, marginal cost should be used in calculating the costs for the low-valued items.) March 1997 Admittedly, the analysis described above is convoluted, imprecise, cumbersome and subject to much judgment. However, it is believed that useful insights will be gained through the discipline imposed by this exercise: Assigning rough values to data needs. 2. Assessing total data needs priorities over a several year time period. 3. Examining the possible cost savings in covering as many data sets as feasible in each collection effort. 4. Examining possible cost savings in reordering the time value of specific data sets. Eliminating redundant data collections and data sets with low- assessed value. As is often the case, insights gained through the discipline imposed by such an exercise are more valuable for decision making than the specific results of the calculations. 2.4 Task 6: Data Integration Issues 2.4.1 Introduction The Task 6 objectives for the Multimodal Transportation Planning Data Study include focusing on the issues and implementation strategies associated with data integration, consistency, and sharing. Specifically the objectives include the following: . assess similarities and differences and the needs for consistent data for multimodal planning; NCHRP Multimodal Transportation 111 Planning Data Project 8-32(5)

Jack Faucett Associates, lac. Final Report - ^Uarch 1997 develop a comprehensive assessment of data integration issues to improve data collection and assembly; examine data integration strategies to relate transportation demand, supply, performance, and impact data. Included in these analytic objectives is the consideration of locational referencing systems, scale and resolution differences, data sharing and access issues, and ~ade-o~s between the use of primary and secondary data. It is also the purpose ofthis assessment to be consistent with the Federal Geographic Data Comrni~ee's National Digital Geospatial Data Infrastructure Framework. As has been Sue for all aspects of this research effort, ache analysis and recommendations regarding integration are not independent of He preceding analysis relating to data needs assessment, organization, collection, arid economic considerations. Discussions regarding data integration and other issues should not be the last in the series of issues which follows some linear format. Knowledge of integration issues and organizational, as well as state or regional implementation strategies or guidelines, is necessary feedback as part of all data program issues (see figure below). Ideally however, the strategies and recommendations already presented (e.g., Business Model, organization framework, data task force, economic evaluations) will have established a solid framework for the implementation of data integration and cooperation strategies. The initial focus of this section is on Be organizational, rawer Pan the technical (i.e., hardware and software needed to implement integration strategies) issues associated with data Integration. Without resolution, or at least discussion, of the Institutional impediments to integration, issues such as data inconsistency, redundancy, and incompatibility will continue to plague the transportation planning community and technological advances may only serve to initiate minor improvements in integration. NON-LINEAR FORMAT OF DATA PROGRAM data Pro ram ~ _ . _ , ~ NCHRP- Multimodal Transportation 112 Planning Data Project 8-32~5)

Jack Faz~cett Associates, Inc. Final Report Marell 1997 The latter part of the section includes a discussion of the use of geographic information systems (GIS) as a tool for integration and presents some of the recent research in this field. The Task 6 analysis concludes with case studies that describe organizations where integration strategies have been implemented in recent years and which could potentially be useful points-of-contact for other organizations seeking to do the same. 2.4.2 The Need for Data Integration and Consistency Def nition of "Integrate " - to form, coordinate, or blend into a functioning or unified whole; to unite. The concepts of integrating data within and between agencies, as well as adopting standards to guide the collection and storage of data are by no means novel concepts in the transportation community or any other public or private organization which encounters data. With a long-standing knowledge and relative lack of activity regarding integration, the question becomes why focus on data integration and, for the purposes of this study, what are the effects of data integration on data collection and organization? Although the answer to the above integration question has been addressed many times in recent years, the response is a logical starting point for a discussion on integration, and serves as a reminder for transportation planners of the importance of this issue. Without question, the two primary reasons for the implementation of data integration strategies are money and time. If cost and time were of no concern to transportation managers, data needs would be met through primary data collection to ensure the quality and scope of all data. All required resources would merely be utilized to meet the data collection requirements. There would be no need for agencies to adapt similar computing systems or collection strategies because no sharing of data would be necessary. Although many agencies and organizations operate in a manner that would lead one to believe the above scenario was true, the reality is that there is an ever increasing constraint placed on transportation managers to function under conditions of decreasing resources with faster implementation time and improved results. As expressed in prior sections, the implementation of ISTEA and the CAAA have increased the analytical responsibilities of transportation professionals and, in-turn the amount of data that needs to be collected. In addition, the emphasis on multimodalism has thrust many agencies together (e.g., transit authority, commuter rail, MPO) that now must share data to a greater extent then was previously necessary. This heightened need for multimodal and environmental data which crosses various organizational boundaries has further illu~runated the difficulties associated with sharing data within and between transportation organizations. In order to effectively share data, there is an immediate need for the interested parties to first be able to access and view each others data, in addition to understanding what is being viewed and having confidence in its quality. This need has given rise to various data l NCHRP Multimodal Transportation 113 Project8-32(5) Planning Data

Jack Faucets Associates, Inc. Final Report March 1997 integration issues such as data definitions, quality of sampling and collection techniques, technology applications, etc. 2.4.3 Issues Related to Data Integration There exists an extensive amount of literature related to data integration and system integration as it applies to transportation due to the previously mandated Management Systems in ISTEA. Many state, local and Federal agencies have spent considerable effort in trying to address data and system integration issues. Though the recent regulatory streamlining effort has relaxed the schedule of ISTEA management system design and implementation, there continues to be a need for uniformity and efficiency in both the collection and organization of transportation data, particularly as it relates to intermodal transportation planning. There are essentially two categories of issues associated with the integration ofdata: those issues that inhibit data integration and those issues that arise due to non-integrated data. The first category of issues is actually data integration impediments and includes institutional impediments, functional impediments, and technology impediments. The second category of issues include unreliable analyses resulting from inconsistent or incomplete data and, inefficiencies in the data collection, storage, and retrieval processes. The discussion of this sub-section focuses on identifying data integration impediments. There are multiple problems associated with data integration. For the purposes of this study effort, constraints prohibiting data integration can be categorized into three types: 1. institutional constraints - multiple organizations within a single jurisdiction often have overlapping data needs resulting in data collection, storage and dissemination redundancy; 2. functional constraints - the various hierarchies and cross-types of transportation organizations require disparate though related data, often at varying levels of resolution, to support similar modeling and reporting requirements; 3. technological constraints - the various hierarchies and cross-types of transportation organizations have different data storage capabilities' data dissemination capabilities and knowledge bases/training resources. Additionally, there is a dynamic evolution of technologies which makes streamlining the data acquisition, storage, and retrieval process very difficult. These technologies include the following: networking; low cost, powerful personal computers; distributed arid cooperative computing; client server network architectures; computer based graphics; geographic information systems; computer-aided design; object oriented data structuring; and innovative data collection technologies. NCHRP-Multimodal Transpor~or' 114 Planning Data Project 8-32~5)

Jack Faucet Associates, lilac. Final Report Starch 1997 It is interesting to note the irony involved with the technological constraints. The very technology that has and will enable data integration and sharing to be realized has materialized as a barrier to successful integration. The myriad of technologies and software, as well as the increasing power of personal computers allows individual organizations and/or departments to operate independent of one another. This can inhibit integration as each entity becomes accustom to individualized software and hardware. 2.4.4 Data Integration Strategies There are generally three strategies used to integrate data, each of which addresses, to varying degrees, the three impediments to an integrated system as discussed in section 3.0 above. These strategies include a centralized approach, a decentralized approach, and a technology based approach. In the first two strategies, data integration relies on the entity that owns and maintains the data. In the centralized approach, one department or group within an organization owns or maintains the data and the user accesses a centralized database system. In the decentralized approach, the user of the data (e.g., the planner), owns, maintains and uses the data. In the third approach, the strategy relies on technology and the entire data collection, maintenance, storage, and user responsibilities are distributed. 2.4.4.1 The Centralized Approach Traditionally, data collection and maintenance responsibilities are turned over to a centralized group, such as an MIS department in smaller organizations, or an entire bureau such as the Bureau of Transportation Statistics, in larger organizations. All data is then accessed via this centralized department. 2.4.4.2 The Decentralized Approach The decentralized approach to data collection and storage has been a bottom-up approach where data was collected and stored on an application-by-application basis with the applications largely uncoordinated with each other. This was the approach taken for data collection and organization before the use of computers. With the introduction of, and reliance on, the personal computer, this approach to data storage and retrieval is beginning to once again dictate the decentralization of all data related activities. From a planning and analysis perspective, decentralized data management may be more efficient. However, there can be gross inefficiencies in both the data collection and dissemination aspects of traditional decentralization. There is a need for an alternative bottom-up approach or an approach that is decentralized but still workable. This alternative approach recognizes the requirement for autonomy by the user but provides mechanized centralization. Rather than having an MIS department coordinate data needs and storage, a centralized referencing system could serve much the same purpose. For example, an integrated multimodal information system might include one file with port berthing information and NCHRP Multimodal Transportation 115 Planning Data Project 8-32~5)

Jack Faucett Associates. luc. Final Report M.arch 1997 another file with port channel depth information, but the relationship between the two files would be a location reference point. 2.4.4.3 The Technological Approach "All of the data required by the ISTEA management and monitoring systems, the Hazardous Waste Act, the Clean Air Act, and, in fact, nearly all of the data managed by transportation agencies in general are, or can be and should be, geographically referenced. Therein lies the key to integration. ,'38 To date, the most researched technology for integrating transportation related data is the use of Geographic Information Systems for Transportation (GIS-T). GIS in its narrowest sense refers to specialized software for the management and analysis of spatial data and Weir attributes. However, in most of the GIS-T related research performed to date, K. Dueker and D. Kjerne's39definition of GIS is cited and expanded upon. Accordingly their definition follows: GIS is a system of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing and disseminating information about areas of the earth. Thus, the definition includes computing capability arid databases; managers and users; and the organizations within which they function arid Me institutional relationships that govern their management and use of information. GIS also refers to a new paradigm for the orga~ii7ntion of information and Me design of information systems. The essential aspect of this paradigm is use of the concept of location as a basis for the restructuring of existing information systems and the development of new ones. The concept of location becomes the basis for effecting Me long-sought goals of data and systems integration. Recent research conducted In the area of GIS and GIS-T will be discussed former later in section 5.7. It needs to be strongly stated at this point Mat GIS is not fi~ndamen~ly necessary to implement data integration. It is merely stressed in this section because much of the related literature and recent management decisions regarding integration have involved GIS-T (see Case Studies - section 5.8). A large centralized relational database that has been inputted with standardized data and has the ability to perform queries that will support transportation planning functions could prove to be an 38Vonderhoe, A.P. et al. Adaptation of Geographic InforTnation Systems for Transportation. National Cooperative Highway Research Program Report 359, Transportation Research Board (TRB), National Academy Press, Washington, D.C., 1993. 39Dueker, K.J. and Kjerne, D., "Multipurpose Cadastre: Terms and Definitions." Annual Convestion of ACSM ASPRS, Proceedings, Volume 5 (1989~. NCHRP-Multimodal Transportation 116 Planning Data Project 8-32~5)

Jack Faucett Associates, lac. Final Report March 1997 equally successful integration tool. GIS has the advantage of being able to present information and allow data queries at the spatial level. The quote opening this section also points out the additional advantage of GIS which is that almost all transportation data can be geographically referenced and, therefore, this spatial component can act as the common identifier in a relational database. For this reason, GIS is seen as a logical system when choosing a data management system. 2.4.5 Implementation "Arriving at solutions to institutional impediments will usually prove more difficult than identifying appropriate technological components. "40 The guidance provided by the results of Tasks 1 through 5 of this research effort should allow individual planning departments to identify and collect the data necessary to optimally perform their transportation planning functions in an isolated environment. Although, as expressed above, the steps which take an organization from data needs identification to collection to organization to storage are not necessarily linear and mutually exclusive. The logical next step will be to address the issues of data sharing and integration between organizations (e.g., State DOT, MPO, transit authority, etc.) and within organizations (e.g., transportation management, safety, maintenance, etc.). The focus, however, as stated by the above quotation, should not immediately turn to the technological issues such as the type of hardware and software needed to provide inter/intrn- organizational communication and data transfer capabilities. ~1 ~1 ~1 ~` 1_ _ _ _ _ 1 ~- 1 ~' The functional and institutional `;o~ls~rmn~s nave ocen snown mrougn organ~zallona~ analysis, as well as through conversations and surveys of MPO and state transportation planners to be. in many cases the bottlenecks imner1in~ integration, regardless of the technology. ---or - ---I Many issues and obstacles will prove to be more easily overcome once integration strategies have been implemented within and between transportation organizations which address the institutional and functional issues (described above). Furler, with technology such as networking, the Internet, software integration (i.e., disparate software being able to communicate and share data with one another), and GIS advancing rapidly, many of the technological problems may be much less overwhelming In the near fixture. The institutional and fi~nctional infrastructure must be in nIace that · ~ ~ ~ ~ ~ '~ ~ e , , ~ wade allow tor successful Integration. Without such infrastructure, there is little chance of integration regardless of the capabilities of the technology chosen. 4t7Durgin, Paul M. "Issues in Strategic Planning". Proceeding from the Geographic Information Systems for Transportation Symposium (GIS-T), Sparks, NV, April 1995. NCNRP Multimodal Transportation 117 Project8-32~5) By. . ~ . Planning Data

Jack Fauceft Associates, Into Final Report _~ 2.4.5.1 Organization Strategy March 1997 For the reason's cited above, the initial focus of this section will be on presenting an integration strategy that can be applied regardless of the technology that will ultimately be chosen, if any. The strategic process is one that can be applied not only to integration issues, but to most proposed changes contemplated at the organizational level. The strategy or strategic plant', as outlined by J. Bryson, is very similar to the Business Model approach outlined under Task 1 of the research effort. The ten basic steps to the strategic planning process are listed below: 2. 4. 1. Initiate and agree upon a strategic planning process. Identify organizations mandates. Clarify organizational mission and values. Assess the organization's external and internal environments to identify strengths weaknesses' opportunities' arid threats. 5. Identify the strategic issues facing the organization. 6. Formulate strategies to marriage these issues. 7. Review and adopt the strategic plan or plans. 8. Establish art effective implementation process. 9. Develop art effective implementation process. 10. Reassess strategies arid the strategic plarming process. Similar to the Business Model approach used for the data needs assessment, the process is iterative and actions, results, and evaluations should occur at each stage. In addition, as eluded to above, the process is technology independent. Regardless of the eventual technology used to implement the strategy, this strategic plan can be utilized as the tool to implement a desired change. Also similar to the Business Model, the plan is based on an organizations goals rather than processes that are previously in place to meet this or other goals or missions. This allows an organization to at least begin the process of establishing the institutional and functional framework necessary for successful integration even prior to the adoption of new technology, if necessary. 2.4.5.2 Task Force "When applied to a function or network that crosses organizational boundaries or to a community, the process [strategic planning] probably will need to be sponsored by a committee or tashforce of key decision makers, opinion leaders, 'influentials ', or 'notables ' representing important stakehold~er ~oups."42 4iBryson, John M. Strategic Planningfor Public and Nonprof t Organizations, lossey-Bass Publishers, San Francisco, 1995. 42Bryson, p. 42. NCHRP-Multimodal Transportatwn 118 Planning Data Project 8-32~5J

Jack Faucett Associates, Inc. Final Report - March 1997 As was the case with the data needs assessment (Task 1), the inter-organizational and interdepartmental nature of creating an integration environment produces a demand for a committee or task force that has both the ability and the power to make decisions regarding their individual organizations and the integration of all the organizations into a system. In state-wide or region-wide transportation planning, problems can arise in establishing a lead role for custodianship of thematic data, creating joint use agreements with local and regional transportation groups, establishing data quality control measures, integrating GIS data from design firms automated databases, etc. In addition, because ofthe number of organizations that need to be involved and the diversity in their sizes and resources, many state agencies will be reluctant to put in place regulatory requirements for cooperating with network integration. For this reason, the integration process will likely be much more time consuming and iterative than strategic planning within a single organization and will have to rely more on consent than on authority. Such consent, requires interagency cooperation and the role of a task force would be invaluable. 2.4.6 Tntegration/Cooperation/Sharing As previously stated, integration is not a novel idea in the transportation community. Then given the cost-effectiveness and productivity benefits of sharing data and reducing redundancy and the resulting collecting and processing costs, why has integration been so slow in progressing? The answer to this question is addressed in the Issues Related to Data Integration section and the description of institutional, functional , and technological constraints is comparable to similar responses to the same question given by many public organizations around the country. The real question becomes how to motivate transportation organizations to begin adopting the organizational, functional, and technological infrastructure and processes that would enable integration to take place. Funding - One of the obvious answers to this question is to use the one resource that all public organizations are acutely aware of, funding. Linking certain integration requirements (e.g., standardization of data) to Federal and/or state funding requirements should have an overwheln~in~ effect on the participation rate of transportation organizations. The success of linking funding to integration prerequisites at multiple transportation organizations can be observed in New Mexico's statewide adoption of the New Mexico State Traffic Monitoring Standards (described further under the Standardization section). State and Federal finding is contingent on the adoption of these standards by MPO's as well as the State DOT. The standards have been in place for over nine years arid have been extremely successful at reducing data redundancy, improving quality, arid allowing for easier transfer arid sharing of transportation data. Competition for Fu'2ds43 - Competition for available state and Federal Finding will become more "~Southern California Association of Governments. Monitoring and Information Sharing: An Approach and Conceptual Framework. Revised September 1994. NCHRP-Multimodal Transportation 119 Planning Data Project 8-32~5)

Jack Faucett Associates, Inc. Final Report demanding and increasingly dependent upon demonstration Marc* 1997 of performance in measurable terms. Improvements in data collection, quality, and analysis stimulated through data integration programs can prove to be an effective advantage in improving the performance of transportation programs. Cost Effectiveness and Productivity - Although there has been little, if any work conducted on the costs and benefits associated with data integration, there is an inherent belief that developing an environment that promotes data integration, sharing, and cooperative collection will ultimately reduce or eliminate redundant collection of data, reduce the time spent maintaining data, improve the quality of data and, therefore, the quality and time spent analyzing data. An example of the potential improvements that can be realized from a sophisticated and integrated data collection and storage system can be found in Michigan's recently adopted $20 million management system (described further in section 8.3). Since the adaptation of the system, the percentage of time spent maintaining data has gone from 70 percent to 30 percent, while the time that can now be spent on analyzing the data has been able to increase from 30 percent to 70 percent. Accountability - Improvements in the public's information access and ability to actively participate (i.e., review and make comments regarding public documents) continue to advance nation wide. In coordination with this is the increased need for cost-effective and efficient transportation. The result will be increased public pressure to hold the government, state or local, more accountable regarding the use of public funds and expenditure of other governmental resources. Air Quality and Congestion - If regional air quality fails to improve or degrades, there will be an increase in both public and regulatory pressure to improve air quality and reduce the congestion causing it. If improvements in decision making and analysis can be engineered through improvements in data integration, it could help to avoid some of the unpopular management tools such as pricing mechanisms and lessen the public pressure. In addition, improvements in air quality could lead to a relaxing of regulatory requirements (e.g., lower attainment status) and a freeing up of resources for other management goals. Implementation by Domina'2t Data Controllers - In many states, there are only one or two transportation organizations, usually the state DOT and a large MPO, if any, that collects and organizes most of the data for the state. The smaller local and regional organizations play a minor role in the collection process and may rely heavily on state data and resources. In such situations, these major players have the opportunity to shape the data management system into a coordinated and integrated system merely by changing their own system. Their influence over the minor organizations will force them to come on-line with the new system or be left to find other sources for their information needs. System-wide Improvements - There are even reasons for data integration which are solely self- motivated. Tangential transportation organization (e.g., New York and New Jersey DOT's), as well NCHRP Multimodal Transportation 120 Planning Data Project 8-32~5)

Jack Faucett Associates, inc. Final Report March 1997 as state and regional organizations can benefit from data sharing and cooperation because many of the congestion and air quality problems flow from one region to the next. For example, poor congestion management in Manhattan can cause similar congestion on the in-bound New Jersey side. Data cooperation between the transportation organizations involved (e.g., Port Authority, Path, highway departments, local planning agencies, etc.) can improve congestion on both sides and relieve some of the public pressure caused by unwanted congestion. 2.4.7 GIS Research Once some of the institutional and functional constraints have been addressed at each organization and system wide, attention should focus on the technological systems that will be most useful in meeting the integration goals of the transportation community. This subsection describes some of the recent research conducted in the area of GIS and GIS-T. As was previously expressed, GIS is not vital to the success of an integration system. Rather, GIS is discussed because of its basis in locational referencing and the logical connection to the geographic components of transportation data. In addition, GIS's ability to display data in a geographic environment allows for a more comprehensive and understandable analysis and communication of the transportation data. 2.4.7.1 National Digital Geospatial Data Framework The impetus toward GIS has been so pronounced that the USGS has formed a committee known as the Federal Geographic Data Committee. It is the mission of this organization to develop a framework within which to collect, store and disseminate digital geospatial data. Essentially, the Federal government is in the process of developing a large-scale data integration framework for spatial data. This framework is described below. Purpose and Goals The framework is a basic, consistent set of digital geospatial data and supporting services that will: Provide a geospatial foundation to which an organization may add detail and attach attribute information Provide a base on which an organization can accurately register and compile other themes of data Orient and link the results of an application to the landscape The framework should be widely used and widely useful: Framework data should be data you can trust and should be certified as complying with standards Framework data should be the best data available NCHRP Multimodal Transportation 121 Planning Data Project 8-32~5)

Jack Faucett Associates Inc. Final Report March 1997 Along with these high-resolution data, the framework should contain consistently generalized' lower resolution data to support regional and national applications: Users must be able to integrate framework data into their applications while preserving their existing investment Framework data should be accessible at the cost of dissemination, free from use criteria or constraints. arid available in non-propr~etary forms Additionally, framework data must include geodetic control; digital orthoimagery; elevation data; transportation (roads, trails, railroads, waterways, airports, ports, bridges, and tunnels. Attributes include a permanent feature identifier and name. Where available, linear referencing systems will be used as the identifier. In addition, roads will have the attributes of functional class and street address rarlge.) hydrography; gove~nental urlits; cadastral. This framework would be operated and maintained by a group of participants that agree to provide digital geospatial data that meet content, quality, policy and procedural criteria including a data producer, area integrator, data distributor, theme manager, theme expert, and policy coordinator. Currently, work is underway to develop an implementation strategy. The implementation will be phased, with the goal to have an initial implementation of national geospatial data framework by the year 2000. 2.4.7.2 GIS-T Research Within the past five years there has been an increasingly wide-spread effort to research the applicability of GIS to transportation modeling. Some of the basic differences between the GIS approach to networks and the transportation modeling approach is depicted in the table below. NCHRP MulI-imodal Transportation Planning Data 122 Project 8-32(5)

Jac* Faucet' Associates, Inc. Final Renort Geographic Information System Multi-pu~pose Data-driven Geographic context Many topologies (point, arc, polygon, network) Chairs structures Spatially-indexed Many fields March 1997 Transportation Model Single purpose Model-driven Abstract context Single topology (link-node) Link-node structures Sort-indexed Few fields Source: Sutton, J.C. "The Role of Geographic Information Systems in Regional Transportation Planning." presented at the Fifth National Conference on Transportation Planning Methods Application, Volume 1 ~ Final Report, June 1 995. Two of the larger GIS-T related research efforts are described below. NCHRP Project 20-27 - NCHRP Project 20-27 was initiated in response to the need to define the basic structure of GIS-T based on current and anticipated needs and characteristics of transportation agencies. A number of different reports were published in conjunction with this project including "Implementation of Geographic Information Systems in Transportation Agencies", "Management Guide for the Implementation of Geographic Information Systems in Transportation" and ``Adaptation of Geographic Information Systems for Transportationt,. Pooled Fund Study - The departments of transportation of 39 states and the District of Columbia combined resources under the sponsorship of the Federal Highway Administration (FHWA) and the New Mexico State Highway and Transportation Department to create a Pooled Fund Study. The title of the study is "Geographic Information System for Transportation ISTEA Management Systems Server Net Prototype". The purpose of the study is to create a systems architecture and demonstration prototype to address the requirements of the management systems mandated by the 1991 ISTEA within the context of a GIS environment. The system architecture will consist of a set of non-proprietary models of the ISTEA statewide and metropolitan planning and project selection arid supporting activities from multiple perspectives: data, functional, technological, arid institutional. These models will provide an organizational and technology independent perspective ofthese functional areas concentrating on providing a consensus based national framework suitable for individual adaptation and modification. It is intended that these models will be developed using information engineering principles, methods, arid tools and will be based on the conceptual framework defined by the NCHRP 20-27 research effort. There are four phases of this research effort. Phase A has resulted in the following: NCHRP Multimodal Transportation Planning Data 123 Project 8-32(5)

Jack Faucett Associates, Inc. Final Report . March 1997 An entity relationship data model illustrating an integrated database supporting all six management systems. 2. An activity model defining the general areas implied by the scope of the ISTEA systems illustrating an integrated approach to transportation program development. 3. An integrated systems architecture illustrating the data flows between these systems. 4. Evaluation of the Information Engineering methods used in the analysis. Phase B of this effort is Demonstration and Design and has resulted in the following: 1. 2. 3. A database design, including table arid column definitions. System pseudo code outlining art integrated approach to systems development. Evaluation of the software engineering methods used to develop these functional specifications The Phase C objective is Demonstration Development and has resulted the following: 1. Integrated databases, integrated computing networks, integrated software codes, integrated command and control systems. 2. Specific examples of ISTEA marla~,ement systems implemented in a GIS-T context. Phase D, Research Results Transfer, is Me method Mat the Study Team proposes for vendors and consults to sponsor this study. 2.4.7.3 Benefits of GIS-T and Integration The most readily apparent benefits of an integrated system arise from the reduced costs of doing business that result Tom enhanced productivity44. The increased productivity is realized Trough the reduction or elimination of redundant data arid the associated collection and organization activities, as well as the updating of multiple data bases managed by different units. Other benefits include: Reduced time/cost of cartographic production arid updates; · Enhancement of thematic maps (e.g., those used for traffic counts); · Quicker response time in creating new traffic analysis zones (TAZs) or revising existing ones; · New capabilities (e.g., linking of land use, transportation, and air quality data arid models); · Increased response time to unexpected events (e.g., emergency evacuation). 44Vonderohe, A.P. et al., "Adapting Geographic Information Systems for Transportation.,' TR News 171, March- April 1994, pp. 7-9. NCHRP Multimodal Transportation 124 Planning Data Project 8-32~5)

Jack Faucett Associates, Inc. Final Report March 1997 The use of GIS can also lead to intangible benefits which are not immediately apparent until after GIS has been implemented. For example, the mapping and visual display of transportation data (e.g., travel time) can allow transportation professionals to more easily identify problem areas aIld locations where new data is needed and can ultimately lead to better decision making and better data collection. 2.4.7.4 Current Limitations of GIS-T4s The above discussion of GIS-T and the associated benefits does not mean to imply that GIS is a panacea for all transportation data collection, analysis, and organization problems. The relative newness of GIS as a transportation tool understandably results in some limitations that are associated with the technology and its capabilities. GIS products provide the means to manage the procedures that link spatial and attribute data. Many ofthe user-friendly GIS software available are designed as "canned" applications which give the user fewer options or macro tools to develop their own application. whereas hiah-nerformin~ .sv~tem~ require a lot of training and programming experience. , (, ~, The following are brief descriptions of common kar~sportation scenarios that may present a GIS system with some difficulty and may prove to be limitations of the system or entail additional GTS editing. 1. ~- .] Network topology problems - A situation such as a bridge passing over a roadway _ which does not provide a connection to the road below can present a problem if routing is the primary objective. Network based route connectivity - Another example where GIS also has difficulty is the situation where multiple transit networks (e.g., bus, light rail, special bus) each operate on the same street with various route constraints. GIS is unable to operationalize the three sub-networks which have different levels of connectivity and topological representation on We base street map. Schematic Network Integration with GTS - An example of these GIS limitations is evident in situations where HOV lanes are part of the primary highway or freeway. Representing these lanes as offsets would build inaccuracy into the GIS network representation. Problems such as these are enhanced when transit networks are also intertwined (e.g., light rail in the mediarl) arid present further difficulties for GIS. 45Su~on, J.C. aThe Role of Geographic Information Systems in Regional Transportation Planning." presented at the Fifth National Conference on Transportation Planning Methods Application, Volume 1, Final Report' June 1995. NCHRP-Multimodal Transportation Planning Data 125 Project 8-32(5)

Jack FG~COtt Associates, Inc. Final Report - March 199, 4. Transportation routing - GIS routing is performed none to node rather than at links. If' however' special situations arise such as U-mrn3 they must be coded individually. The frequency ofthese special situations can resultin aninordinate amount of coding and prohibit GIS routing from being cost-effective. 2.4.8 Case Studies 2.4.8.1 ~ Standardization A necessary first step for successful data integration and sharing involves standardizing the methodologies by which data is collected and organized. New Mexico - The New Mexico State Highway and Transportation Department has been implementing its Traffic Monitoring Standards since October 1, 1988. The impetus for the development of standards were the problems being caused by multiple definitions for the same data, data being reported from non-working counters, incomplete data being filled in various ways and with varying amounts of disclosure. The standards are annually reviewed and participation is open to all New Mexico transportation professional in both the public and private sectors. The guidelines adopted by the American Association of State Highway and Transportation Officials (AASHTO), the American Society for Testing and Materials (ASTM), and the Federal Highway Administration (FHWA) are used as the default standards if is not addressed by the New Mexico standards. The standards describe acceptable methods for data collection, such as minimum periods for data collection sessions, sample size, and equipment testing guidelines and operational tolerances. The key to the New Mexico's success in standardizing monitoring traffic monitoring standards is its link to funding. In order for traffic monitoring to receive state or Federal funding, it must be in compliance with the New Mexico State Traffic Monitoring Standards. If, due to a lack of resources, there is no established standard for a particular monitoring practice, the monitoring must be done at least to the same level as practiced by the New Mexico State Highway and Transportation Department. The adoption of standards also established an excellent foundation for the rest of the necessary elements for a successful data program. For example, the uniform methods allowed the design and implementation of processing software to be more easily achieved. In addition, the standards provided a common language for characterizing and analyzing statistics on various agency reports. NCHRP Multimodal Tr~sporta~on 126 Planning Data Project 8-32(5)

Jack Faucea Associates, .,nc. Final Report 2.4.8.2 Data Sharing March 1997 Bay Area Partnerships - The Bay Area in California covers 7,000 square miles, includes over 100 cities, with approximately 6 million residents. The transportation system includes 1 8,000 miles of roadway and eight primary public transit systems. The complexity of the system was a major impetus for the Bay Area Partnership which consists of the top managers from 31 agencies responsible for transportation and environmental quality in the region. The Data Integration Task Force was formed by the Partnership to examine the issues regarding data collected and used for planning and managing transportation, land use, air quality, and other environmental issues. The three primary objectives of the Task Force included: I. Increase joint use of infonnation and meet multiple needs for data more efficiently 2. Identify additional data needs and fill data gaps where warranted. Identify opportunities to streamline current data collection and dissemination processes. In an attempt to meet these objectives, the task force conducted a survey of many of the agencies within the Bay Area regarding their available data. The result of the survey was a catalog which includes data available from certain agencies and issues related to the data sources such as where the data is reported, the method of collection, location frequency, etc. In addition, where applicable, the discussion the use of GIS and accessing the information through the Internet is provided. 2.4.8.3 GIS-T Precedent Not only are large research studies into GIS-T being performed, but actual GIS systems are being implemented throughout the country. Descriptions of some of these systems being implemented for transportation planning purposes are provided below. Michigan DOT - The Michigan Department of Transportation (MDOT) is in the final stages of the development of the most comprehensive Transportation Management System in the country. The project was begun in 1992 arid two of their main objectives were to 1) eliminate or reduce duplication arid 2) implement GIS. The MDOT has invested approximately $20 million in Me this two-tier client server system which houses over 600 data tables. The development of the system was a top-down effort designed by users. The data is organized around the management systems outlined In ISTEA and is able to be accessed remotely via modem by over planning agencies in the state. 46Metropolitan Transportation Commission (MTC). Data Integration Project Catalog of the Bav Area Partnership, MTC, Oakland, CA, March 1996. NCHRP Multimodal Transportation 127 Project 8-32~5J Planning Data

Jack Faucea Associates, ~c. Final Report March 1997 The new system has already proven to be effective in allowing MDOT professional to make beher use of their time with respect to transportation data maintenance and analysis. The percentage of time spent maintaining data has gone from 70 percent to 30 Dercent. while the time .~nent I data has gone from 30 percent to 70 percent. ~r - ^~ I- ~ Similar to the Data Task Force proposed by MA as part ofthe Data Program, the MDOT uses a data committee, consisting primarily of state management system experts, that oversees the cooperating in data collection and decides which organizations are going to collect what data. I/~ A_ ~ By ~_ ~t ~1 Or A ACTS · . ~- _ I ~ Briny en `runspariUllan (~AQ1J - 1ne VAQ1 IS currently developing an integrated transportation information system (ITIS). It is one of the first efforts at an ITIS in a state DOT context. Their goal is to unify databases across separate agency divisions to allow common access to all data and to provide an integrated environment to meet agency needs. NCTCOG - North Central Texas Council of Governments has begun using GIS as part oftheir long range transportation planning program for the Dallas-Fort Worth Urban area. GIS software is used for spatial analysis, data coding, and attribute display in support oftheir travel forecasting model. NCTCOG maintains four primary data sets for input to their travel forecasting model: 1) the regional highway network, 2) the regional transit network, 3) zonal attributes and 4) traffic count data. Prior to implementation of the GIS, maintenance of these data sets required many separate computer programs and considerable mar ual effort. Now with the GIS the data sets can be easily edited and updated using the GIS graphical interface and the results verified using a variety of thematic maps. Wisconsin DOT - Wisconsin DOT has a expert system-GIS for pavement management. The GIS provides the tools to develop the spatial database required for input to the expert system. The expert system codifies the knowledge and experience of pavement engineers in evaluating pavement condition and making recommendations for maintenance and improvements. Central Artery/Tunnel project in BOStOM - Boston used a GIS as an integral part of the program for automation of project management, engineering and construction. In addition to preliminary highway design files, final highway design files are produced for the project using a GIS/CADD system. Other GIS applications include generation of soil profiles, identification of ROW needs and environmental remediation sites, traffic surveillance and control during construction and identification and mitigation of adverse construction impacts on the community. Newton, Massachusetts - Newton has been forward thinking in evaluating how to better manage and analyze its planning and engineering data and is currently developing a citywide GIS. Newton built _ 1 ~ 1 1 · ~ Y ~ · . . · ~ ~ . ~ . applications within a Lip environment to include traffic assignment, routing applications (such as school bus route generation), network location problems (such as fire station location), and traffic zone reapportionment. In this system the analytical tools reside as modules within the GIS as NCHRP MultimodalTransportation 128 Project8-32~5) Planning Data

lack Faucet! Associates, lac. Final Report - March 1997 opposed to either transferring data through ASCII files to the model or interactively using a common database. Atlanta - The objective in developing a prototype GIS-T was to design a svelte to help the r.~,niv agency better manage its transportation program. _0~ ~--~ r ~ ~ ~ ~ ~ ~ ,, Application modules included in the prototype are an integrated a accident record system, traffic engineering, pavement management, transportation planning and land use, and transit. Charlotte' North Carolina - A GIS-T was developed to conduct an analysis of ~nter-area commuting patterns. TransCad was the GIS package modified for this analysis. Traffic was simulated over the network and preliminary forecasts of traffic were made. Additionally, LAND SAT imagery is being used to identify and categorize land uses in alternative corridors for the parkway. The three types of data included in the system are network description and related data, population and employment data, and trip data. 2.4.9 Future of GIS-T and Integration As exemplified by many ofthe case studies above, there has been a definite push towards integration and the incorporation of GIS in the transportation community in recent years. Due to the speed with which new technology and software is developed and brought on-line, the future of transportation information systems is somewhat unknown. Some of the possible developments are discussed briefly below. Movement towards ar' open system - The diverse number of transportation applications produces a need for tools that cart utilized in coordination with other information technologies to leverage their use most productively. which allow linkages to programs that the user desires which may be external to the specific product. In pursuit of this goal, the Federal geographic Data Committee and Spatial Data Transfer Standards have encouraged GTS vendors to develop software by 1997 that will allow spatial data to be transferred between platforms. I'2 creased use of object orientedprograms - The Pooled Fund Study described earlier has completed extensive research on adapting art object-oriented approach to GIS. Utilizing the defined objects, the project constructs specific application data models that address transportation problems. One of the primary benefits of the research is that it is providing usefi~1 object definitions. Integration of Intelligent Transportation Systems (ITS) and GIS-T- Although many ITS developers claim that GIS is inadequate for their needs, areas have been identified where the NCHRP Multimodal Transportation Planning Data 129 Project 8-32~5)

Jack Faucett Associates, Inc. Final Report - combination of the two technologies may prove benef~cial. These include the use of dynamic graphics for traffic monitoring and using GIS as part of real time customer information systems.47 March 199? - 2.4.10 Conclusion The regulatory and public pressures demanding cost-effective transportation management to relieve both transportation and environmental problems is rapidly influencing the evolution oftransportation information systems. State and regional organizations are quickly realizing that, in order to meet these demands within their diminishing budgets, data integration, sharing, and cooperation can no longer be thought of as a distant reality when technology and resources become available. Steps must be taken today which will initiate the integration process and allow the cost savings and quality improvements in data and analysis to be realized. Care must be taken not to immediately affix time and resources to addressing the technology issue. Although GIS appears to be where the industry is headed, merely installing a GIS system will not equate to data integration. The main constraints to integration will be found through an institutional and functional analysis' ~ A1 ~1 ~1 ~1 , 1 ~- both within organizations and system wide. ;~1(l(ll-f-~:~lrl ~IO0~!0 lC?Oll-C! ^~- ^= tO^lrl~r ~ · ~^ 4~1 ~At `1_ _ 1 , The methodology for v_ ^~ __ &~ ALAR t>w Balm 111 Ill-will ~ ~11~ way as me Data needs assessment outlined in Task 1. The Business Model and the strategic plan to initiate integration outlined above follow the same logical path and it may prove more cost-effective to address and perform both exercises simultaneously. Integration is also similar to the prior discussions on data needs and organization in that the need for a task force to handle the coordination of the integration process is not only necessary but probably vital to the success of the program. Committee's such as the Federal Geographic Data Committee may handle the standardization of GIS at the national level, but committee's are needed to address the institutional and functional constraints as well at the state and regional level. . 47Sutton. NCHRP Multimodal Transportation Planning Data 130 Project 8-32~5)