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11 STRATEGIC VISION FOR MOVING FORWARD The SHRP 2 C20 research effort culminated in the development of 13 research areas, described in this strategic plan as sample research initiatives. Collectively, these sample research initiatives constitute a programmatic approach for systematically improving freight modeling and data availability and forecasting and planning tools. These ini- tiatives are tied to the seven strategic objectives listed in Chapter 2, with the ultimate goal of promoting and cultivating innovation through Strategic Objectives 2 and 3, supported by the innovations in data development in Strategic Objective 4 and visual- ization in Strategic Objective 7. Each of the sample research initiatives relates to one or more of the three main research dimensions identifi ed at the 2010 Innovations in Freight Modeling and Data Symposium: ⢠Knowledge relates to a general understanding of freight transportation issues and the extensive array of elements involved in planning and forecasting freight demand; ⢠Models are the tools used to plan and forecast freight transportârelated activities at various geographic levels; and ⢠Data are the underlying information resources for modeling and planning efforts; these data often represent an important limitation of modeling. The sample research initiatives are summarized in Table 5.1. For full descriptions and implementation considerations, see the SHRP 2 C20 technical report. These sam- ple research initiatives are based on the SHRP 2 C20 research conducted, but they should be viewed in their proper context as steps in support of the seven strategic objectives. The specifi c research projects detailed in the technical report are initial 5 SAMPLE RESEARCH INITIATIVES
12 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN TABLE 5.1. SAMPLE RESEARCH INITIATIVE Sample Research Initiativesa⦠Research Dimensions Strategic Objectives Knowledge Models Data 1. Improve and expand knowledge base. 2. Develop modeling methods to reflect actual supply chain management practices. 3. Develop modeling methods based on sound economic and demographic principles. 4. Develop standard freight data to smaller geographic scales. 5. Maximize use of freight tools by public sector for planning and programming. 6. Improve availability and visibility of data between public and private sectors. 7. Develop new and enhanced visualization tools and techniques. A: Determine the freight and logistics knowledge and skill requirements for transportation decision makers and professional and technical personnel. Develop the associated learning systems to address knowledge and skill deficits. l n B: Establish techniques and standard practices to review and evaluate freight forecasts. l n o o C: Establish modeling approaches for behavior-based freight movement. l l n D: Develop methods that predict mode shift and highway capacity implications of various what-if scenarios. l l n n E: Develop a range of freight forecasting methods and tools that address decision-making needs and that can be applied at all levels (national, regional, state, metropolitan planning organization, municipal). l l n n o F: Develop robust tools for freight costâbenefit analysis that go beyond financial considerations to the full range of benefits, costs, and externalities. l l o n G: Establish analytic approaches that describe how elements of the freight transportation system operate and perform and how they affect the larger overall transportation system. l l n o H: Determine how economic, demographic, and other factors and conditions drive freight patterns and characteristics. Document economic and demographic changes related to freight choices. l n I: Develop freight data resources for application at subregional levels. l o o n J: Establish, pool, and standardize a portfolio of core freight data sources and data sets that supports planning, programming, and project prioritization. l n n o K: Develop procedures for applying freight forecasting to the design of transportation infrastructure, particularly pavement and bridges. l n L: Advance research to effectively integrate logistics practices (private sector) with transportation policy, planning, and programming (public sector). l o n n M: Develop visualization tools for freight planning and modeling through a two-pronged approach of discovery and addressing known decision-making needs. l l l n Note: Directly Addresses Objective n Indirectly Addresses Objective o a The sample research initiatives outlined as part of the SHRP 2 C20 research project demonstrate how the strategic objectives could be advanced. Each initiative also applies to one or more of the three research dimensions (indicated by l).
13 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN recommendations for potential research to help move this process forward, but many of these are likely to change based on funding sources, industry needs, and develop- ments that spring from some of the other elements of the Strategic Plan (e.g., the GFRC and future data and modeling symposia). Given that freight transportation is highly dynamic, the research required for plan- ning and decision-making support will evolve over time. An important function for those involved in implementing the Strategic Plan is to periodically assess changing research needs relative to decision-making requirements. SAMPLE RESEARCH INITIATIVE A Determine the freight and logistics knowledge and skill requirements for transporta- tion decision makers and professional and technical personnel. Develop the associated learning systems to address knowledge and skill deficits. Description The complex factors that drive freight transportation demand require an understand- ing of economics, land use, public policy, demographics, finance, and information technology. The education and development of professionals involved in freight plan- ning and forecasting will be an effective strategy for improving freight planning, analy- sis, and decision making. Successful planning and forecasting in freight transportation can be enhanced through the dissemination of knowledge among professionals whose current training is likely to be oriented toward passenger travel or general transporta- tion issues. The intermodal revolution of freight transportation in the past several decades, for example, was primarily aimed at enhancing efficiency for private sector interests. The dramatic transformation of freight transportation during recent decades, however, was not reflected in advances in planning practices by state DOTs or MPOs. These advances in freight transportation practices were largely driven by advances in infor- mation technology and information management. Consequently, this research is aimed at sparking a revolution in freight planning and forecasting through a broad-based initiative to enhance knowledge of public and private sector interests. The lack of uniformity in knowledge levels about freight issues in the transportation planning community is compounded by a significant disconnect between the goals and objectives of shippers and carriers in the private sector and planners in the public sector. This research identifies skill sets and techniques to help bridge these gaps and facilitate more effective planning and problem solving. An ideal skill set includes an integrated curriculum with subject areas such as computer science, planning, economics, political science, and organizational skills. Communication skills are also critical for profes- sional development in this area. This initiative will be implemented in three major research phases: 1. Conducting an extensive knowledge and skills requirements analysis for all levels of transportation professionals and decision makers.
14 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN 2. Over time, developing, piloting, and evaluating comprehensive knowledge transfer subject matter and media. This phase will include a wide range of approaches, even including brief employment swaps. 3. Developing the supporting organizational and structural approaches, such as national and regional freight innovation academies, to effectively deliver an ongo- ing knowledge and skills delivery system. Benefits and Expected Outcomes ⢠Enhanced performance of individuals and organizations through a greater knowl- edge of freight and logistics; ⢠Greater understanding of the need for public planning and analysis to incorporate freight and logistics; and ⢠Greater collaboration between public and private sectors to provide a much- needed understanding of the discrete segments of the freight transport community, including shippers, carriers, customers, and other elements of the supply chain. SAMPLE RESEARCH INITIATIVE B Establish techniques and standard practices to review and evaluate freight forecasts. Description Freight modeling, like passenger travel demand modeling, has forecasting capabilities that are used to estimate the movement of freight on highways, railroads, and other elements of the freight transportation system. The effectiveness of these modeling tools is rarely analyzed, mainly because validation processes completed years after forecasts are done are perceived to be of dubious value in light of how rapidly circumstances may change during the planning period. This research aims at developing practices to review previous freight forecasts over short- and intermediate-term horizons, with a review of factors used in forecasts and a backcasting comparison of actual freight values, mode shares, and other characteris- tics of freight transportation. In light of the ongoing developments in freight forecast- ing and the fact that the most robust forecasting tools used by public agencies have been developed within the last two decades, the validation of long-term projections is not considered a near-term research priority. This research effort will be oriented toward the freight forecasting methods docu- mented in NCHRP Report 388: A Guidebook for Forecasting Freight Transportation Demand (Cambridge Systematics 1997) and in National Highway Institute Course 139002: Uses of Multimodal Freight Forecasting in Transportation Planning (Federal Highway Administration 2010). This research involves a historical survey of 15 to 20 public agencies that (1) have used one or more of the various tools described in these publications and (2) have documented or published results from their freight forecast- ing processes that can be assessed at the present time. The validation research is based on a mix of short-term (up to 3 years from original forecast) and intermediate-term
15 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN (3 to 10 years from original forecast) results. The survey will document projected versus actual conditions for these 15 to 20 models as measured by the facility (high- way, rail, and terminal) operating characteristics (volumes), mode choice, routing, and commodity flows. The use of these measures will depend on their applicability to individual models. Benefits and Expected Outcomes ⢠Improved freight forecasting through a structured learning process related to actual versus projected conditions; ⢠Development of model calibration tools to improve models over time; ⢠Guidance on additional data and other factors to be incorporated into the plan- ning and forecasting processes; and ⢠Insight into how various factors used in previous freight forecasts can change over time and influence each other in ways not previously considered. SAMPLE RESEARCH INITIATIVE C Establish modeling approaches for behavior-based freight movement. Description Analytic tools are required to model or forecast freight flows and modal volumes in ways that generally reflect the decision making of shippers, carriers, and receivers of goods. These tools will assist state DOTs and MPOs in better planning and prioritizing system investments and assessing and measuring system performance. Private sector freight stakeholders must work with public sector stakeholders to establish model parameters, processing methods, and product elements. The end goal of this initia- tive is to establish tools that better model the freight movement patterns of various segments of the industry. Behaviors are not monolithic; that is, long-haul operations behave differently from hub-and-spoke operations, which behave differently from local dray operations. Each has different behaviors and operating characteristics, such as time of day, preferred routes, parking needs, and congestion contribution levels. The research will cover the equipment choices, motivations, and economic choices germane to individual segments and stakeholders of the freight transport commu- nity. Specifically, each research task will provide an in-depth and complete look at a single segment of the industry. Examples include detailed exploration of deliveries to various-sized grocery stores, restaurant delivery of food and beverages, fuel delivery, and parcel package delivery. The tasks in this project will serve as building blocks in the development of a more comprehensive overall freight transport model. Much like subroutines embedded in a highly complex program, this research will provide a modeling approach that includes decision tree creation methodology. Analytic tools are required to model or forecast freight flows and modal volumes in ways that generally reflect the decision making of shippers, carriers, and receivers of goods.
16 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Benefits and Expected Outcomes ⢠Provide a much-needed understanding of the discrete segments of the freight trans- port community, including shippers, carriers, customers, and other elements of the supply chain; ⢠Help public sector agencies gain a better understanding of the impacts policy deci- sions have on individual freight transport segments; ⢠Develop a well-rounded and representative understanding of freight movement that does not generalize or assume that freight movement activity is similar across different industry sectors; ⢠Provide insight on service availability, pricing, and reliability as performance mea- sures for different industry sectors; and ⢠Develop improved understanding of intermodal freight movement. SAMPLE RESEARCH INITIATIVE D Develop methods that predict mode shift and highway capacity implications of various what-if scenarios. Description Freight is substantially more varied than passenger transport in the complexity of its transportation processes and its global multimodal and intermodal nature. Some trucking companies are now transportation brokers, making customer service, cost, and the freightâs delivery schedule the focusânot the mode. State DOTs and MPOs need to better understand how freight shifts across modes and how highway capacity is affected by such shifts. What-if scenarios are valuable for planning and testing alter native investment scenarios. Considerations in such scenarios include fuel costs, congestion pricing, toll increases, new or closed rail spurs, and improved waterway infrastructure. This area of research identifies the key decision points and factors that dictate mode shifts, route selections, equipment selection (e.g., size and type of truck, con- tainer or noncontainer), trip frequency, and so forth. These decision points and factors vary greatly in any given situation. In addition to the example considerations identi- fied above, variables include policy changes, customer demand, weather, infrastructure capacity, transportation company mergers, and strategic partnership development. Completed research will provide a decision tree model to illustrate what-if scenarios. The research effort described in this initiative will build on the underlying eco- nomic and demographic foundations of traditional econometric models used in freight forecasting. There will also be enhancements related to considering intermodal trans- fers, the growing role of third-party and fourth-party logistics providers in the freight transportation industry, and ongoing refinements in supply chain management.
17 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Benefits and Expected Outcomes ⢠A tool provided for public agencies and private entities that can help determine unforeseen effects caused by a variety of factors facing the freight community on a regular basis; ⢠An opportunity for public agencies to consider the impacts associated with infra- structure investments (or lack of investments) and also to create realistic contin- gency plans; ⢠A better understanding gained by public sector agencies of the impacts that policy and infrastructure investment decisions may have on individual elements of the freight transportation system or geographic regions; ⢠Similar to Sample Research Initiative C, improved insight on service availability, pricing, and reliability as performance measures for different industry sectors; and ⢠An improved understanding of freight movement and the role of intermodal trans- fers and service providers (including less-than-truckload carriers and third-party and fourth-party logistics providers) in freight transportation. SAMPLE RESEARCH INITIATIVE E Develop a range of freight forecasting methods and tools that address decision-making needs and that can be applied at national, regional, state, MPO, and municipal levels. Description Many of the techniques used in forecasting freight movement are oriented toward spe- cific geographic scales reflecting varied planning needs. Some of these methods require tools and data that are specific to one geographic scale and may not translate well from one geographic scale to another. Data sources that are most applicable to more coarse geographies (e.g., FAF, Transearch) do not translate to local levels, and local freight planning techniques are usually vehicle-based, are inextricably linked to land uses, and do not take into account the broad economic factors that drive freight movement. In addition, wide differences exist between the warehousing and distribution practices for different commodity types (e.g., the delivery process for a local food distributor versus a multistate distribution process for major retailers). This research will bridge the gap between the varied geographic scales used in freight planning by establishing a set of tools as a foundation that can be applied to different geographies depending on need. This foundation can be laid by completing research into ⢠Freight and truck generation rates for different types of land uses and commodity types by trip type (local, long-haul, drayage) and direction (inboundâoutbound); ⢠Different practices for warehousing and distribution for various commodity types, geographic areas, and population densities; and
18 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN ⢠Approaches to combine this information with data that are readily available on a broader geographic scale through existing industry sources in order to create plan- ning and forecasting processes for up to four geographic scales. Sample Research Initiative E is similar to Sample Research Initiative C and will build on that research effort. Sample Research Initiative C relates to freight data, while Sample Research Initiative E involves enhancing analytic processes that build on previous and ongoing research in this area. Previous work related to this research effort includes NCFRP Report 8: Freight-Demand Modeling to Support Public- Sector Decision Making (Cambridge Systematics and GeoStats 2010); NCHRP Syn- thesis of Highway Practice 384: Forecasting Metropolitan Commercial and Freight Travel ( Kuzmyak 2008); NCHRP Report 606: Forecasting Statewide Freight Toolkit ( Cambridge Systematics et al. 2008); NCHRP Report 388: A Guidebook for Forecast- ing Freight Transportation Demand (Cambridge Systematics 1997); and others. The multitiered planning and forecasting processes documented in National Highway Insti- tute Course 139002: Uses of Multimodal Freight Forecasting in Transportation Plan- ning (Federal Highway Administration 2010) can also provide direction for this effort. The approach for this initiative involves research at four geographic levels: 1. Regional (e.g., mid-Atlantic region); 2. State (e.g., Pennsylvania); 3. Major metropolitan area (e.g., Philadelphia); and 4. Minor metropolitan area (e.g., Harrisburg). The research involves documentation of current freight planning practices for the types of areas listed above and research on freight activity (by all modes) within each geographic level, including special generators such as rail terminals and port facili- ties. Internal links between generators and intermediate destinations (e.g., inter modal terminals and regional distribution centers) will be documented, along with links between these intermediate destinations and final freight delivery locations (e.g., retail establishments). Freight activity will be documented by mode, commodity, origin and destination (internalâinternal, internalâexternal, externalâinternal, and through move- ments), and other pertinent characteristics for each geographic level. This effort will be carried out in close collaboration with the Sample Research Initiative C effort, as the final recommended research element of Sample Research Initiative C specifically ties to the four geographic areas described here. An innovative element of this effort is the use of data resources that may not be used in traditional freight forecasting practices, such as state labor departments, economic development authorities, and similar public agencies. The development of data fusion tools, collective industry knowledge, and advanced technology (e.g., GPS data, radio frequency identification technology for inventory control) to support these efforts is a potential outcome of this research. Further efforts to link these data to existing data resources used in traditional planning practices (e.g., FAF Version 3, Transearch) are also envisioned as an outcome of this initiative.
19 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Benefits and Expected Outcomes ⢠Document current freight planning and forecasting practices for different geo- graphic levels, along with ongoing research related to potential enhancements of these practices; ⢠Provide a detailed view of freight planning and forecasting considerations for dif- ferent geographic levels; ⢠Link enhanced data resources from Sample Research Initiative B to new or enhanced forecasting methods for different geographic levels; ⢠Develop new tools for linking disparate data resources not traditionally used for freight planning and forecasting; ⢠Establish a correlation between or supplement to local data (including non- traditional data) and commodity flow data available for broad geographic scales, including national data sources such as FAF and Transearch; and ⢠Develop methods for nesting local freight planning data and tools into those that are used for larger scales (i.e., developing local tools that function as subsets of national tools and data resources). SAMPLE RESEARCH INITIATIVE F Develop robust tools for freight costâbenefit analysis that go beyond financial consid- erations to the full range of benefits, costs, and externalities. Description Freight movement and logistics are significant components of the nationâs economy and gross domestic product. Transportation agencies are looking for ways to better link transportation planning decisions with economic development and other factors, both costs and benefits. This research is aimed at helping to better understand and estimate the full range of monetary and nonmonetary freight costs and benefits in sup- port of more informed decision making and analyses of policies, programs, projects, and investments. The research objectives include the development of measures that can standardize the disparate costs and benefits for use in an overall costâbenefit analysis. This effort includes metrics such as congestion, air quality, employment, social equity, property value impacts, community livability, diversification of economic activity, system redun- dancy, and safety and security. Several research efforts provide guidance on nonmonetary metrics related to freight transport. These include Shipper Willingness to Pay to Increase Environmental Performance in Freight Transportation (Fries et al. 2010) and Building Resilience into Freight Transportation Systems (Ta et al. 2010).
20 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Benefits and Expected Outcomes ⢠Identify nontraditional components necessary for a comprehensive, holistic costâ benefit analysis; ⢠Document and include external benefits and costs in infrastructure investment decisions; and ⢠Garner support from a range of stakeholders, including those not directly involved in transportation, in the planning and decision-making processes for major trans- portation investments. SAMPLE RESEARCH INITIATIVE G Establish analytic approaches that describe how elements of the freight transporta- tion system operate and perform and how they affect the larger overall transportation system. Description One of the challenges in freight transportation planning is identifying how local and regional freight operations are affected by passenger travel and land uses that potentially conflict with freight activity, and vice versa. Issues to be examined include impacts of trucking activity related to congestion on the highway system during com- muter peak periods, passenger and freight rail conflicts on shared rail alignments, and the effect of local ordinances limiting freight activity near certain types of land uses (e.g., residential, institutional). The purpose of this research is to develop, pilot, and validate new analytic tech- niques to effectively integrate freight movement behavior with passenger movements. The intent of this research is to demonstrate the interaction and impacts of freight on the overall transportation system. The research needs to be sensitive to variations in long-haul shipping and local deliveries as each relates to land use patterns, population density, and underlying (non-freight-related) congestion on the transportation system. This sample research initiative attempts to identify the relationship between freight activity and infrastructure and land use constraints related to competing transpor- tation needs and land uses that do not complement intensive freight activity. These efforts will be accomplished through research into ⢠Decisions related to transport mode and route choice due to congestion on a freight transportation network; ⢠Relationships between population density and freight activity on local and regional levels; ⢠Changes in delivery schedules by time of day based on constraints during periods of peak passenger travel; and ⢠Variations in freight activity by time of day under local regulatory constraints (e.g., zoning ordinances restricting freight activity during overnight periods). The purpose of this research is to develop, pilot, and validate new analytic techniques to effectively integrate freight movement behavior with passenger movements.
21 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN The research as it relates to the second and fourth items above will build on some of the temporal freight data documented extensively in Chapter 3 of NCFRP Report 8: Freight-Demand Modeling to Support Public-Sector Decision Making (Cambridge Systematics and GeoStats 2010). The approach for this initiative involves research in three metropolitan regions of North America, with existing travel demand modeling tools in place that have been subject to peer review and have been used extensively for general transportation plan- ning. The three regions include (1) a metropolitan area with freight movement activity associated with freight generators such as manufacturing and warehousing centers and port and rail terminals (e.g., Columbus, Ohio); (2) a metropolitan area where most of the freight activity is associated with local consumer demand (e.g., New York City); and (3) a metropolitan area that serves as a major freight hub even as it supports a sizable local consumer market (e.g., Chicago or Los Angeles). The cities used as the basis for the research documented in NCFRP Report 8 will be examined to determine their applicability to this effort. Targeting these metropolitan areas will provide this research effort with an extensive array of GPS data. Although the research documented in Chapter 3 of NCFRP Report 8 was done using the Federal Highway Administrationâs (FHWAâs) Highway Performance Monitor- ing System and Vehicle Travel Information System data, the present research is aimed at taking this type of base data and documenting the relationship between local truck- ing activity and local roadway congestion. A second element of this research involves a survey of business establishments in each of the three metropolitan areas to document how local congestion on the freight transportation system (primarily highway, but rail congestion will also be addressed) affects the business and operating decisions of ship- pers and carriers with respect to mode choice, operating hours, delivery processes, and routing. This research is qualitative by nature, but these business practices need to be quantified to the extent possible (e.g., âwe start our driver shifts at 5:00 a.m. to avoid highway congestionâ or âwe have to use 30% more drivers today than we did 10 years ago due to increased congestion and fewer turns at the port terminalsâ). Work related to congestion pricing as it pertains to trucking may be applicable for this research as well, such as Tolling Heavy Goods Vehicles: Overview of European Practice and Lessons from German Experience (Broaddus and Gertz 2008). Benefits and Expected Outcomes ⢠Improved understanding of how freight movement affects the overall transporta- tion system at a corridor, regional, or possibly larger geographic scale; ⢠Improved analytic tools to enhance the ability to develop long-range transportation plans that meaningfully consider goods movement, especially in the evaluation of long-term needs and investment alternatives; ⢠Establishment of better coordination between transportation and land use planning; ⢠Improved means to evaluate alternative system capacity investment scenarios; ⢠Improved means to evaluate transportation operations, including ITS applications;
22 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN ⢠Provision of a means to enhance publicâprivate mutual understanding and col- laboration for freight planning and analysis; ⢠Support to develop meaningful transportation system performance measures; ⢠Development of common metrics to use for freight planning and modeling for similar geographic scales (when applicable); ⢠Improved means to understand the intermodal freight movement; and ⢠Enhanced means to understand reliability as a performance measure for freight movement. SAMPLE RESEARCH INITIATIVE H Determine how economic, demographic, and other factors and conditions drive freight patterns and characteristics. Document economic and demographic changes related to freight choices. Description Freight movement is part of a complex supply chain involving the movement of raw materials and products from a source to a point of consumption or to an intermediate point for manufacturing or distribution. The characteristics of this supply chain are heavily influenced by economic factors such as access to labor, markets, transportation infrastructure (various modes), and capital. This research topic involves the development of correlating factors between market conditions for consumption and production and their impact on freight move- ment for different commodities. In addition, the economic benefits of freight activity and the relationship between freight movement and land use needs and decisions will be explored. This research is built on current principles and practices in econometric modeling, with additional research into factors beyond population and labor (i.e., age and income-based modeling and other demographic factors). This research also includes a variation of econometric modeling to assess economic and demographic changes that may result from decisions by shippers and carriers related to site selec- tion, operations, and other considerations. This effort can be informed by recent and ongoing research related to local trip generation, land use, and zoning, such as NCHRP Synthesis of Highway Practice 298: Truck Trip Generation Data (Cambridge Systematics and Jack Faucett Associ- ates 2001) and NCFRP Project 25 [Active]: Freight Trip Generation and Land Use (HolguÃn-Veras 2011). However, the aim of this research is to go beyond traditional factors related to freight movement in terms of land use, industry types, and other issues. One key outcome of this effort is to document the economic benefits of freight activity related to various industries rather than the impacts of freight activity on infrastructure. The industrial real estate development and brokerage communities will likely serve as good sources of information for this research.
23 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Benefits and Expected Outcomes ⢠Establish correlating coefficients and analytic tools that can be applied to regional and state transportation plan development; ⢠Enhance existing econometric models to reflect additional factors that drive freight transportation demand; ⢠Provide supporting data to enhance costâbenefit analysis tools for infrastructure investment decision making; and ⢠Support more robust analyses of alternative investment scenarios by including eco- nomic development and land use considerations. SAMPLE RESEARCH INITIATIVE I Develop freight data resources for application at subregional levels. Description A major breakthrough for freight data analysis and modeling will be the ability to conduct meaningful analyses at small geographic levels that are not currently sup- ported by national-level or large metropolitan area data sets. This research includes the refinement of these current data sources or development of new data sources on smaller geographic scales (e.g., by county, municipality, or zip code). In addition, this research incorporates existing and emerging freight-related data to include permitting data, weigh-in-motion data, license plate reader data, toll agency data, Highway Per- formance Monitoring System traffic count data, and others. All of these resources are to be measurable at the local and corridor-specific levels. This research identifies the data currently collected by state and local jurisdictions and recommends standard formats for consolidating these unlinked data sources so that collectively they can provide reliable assessments of freight movement at a more refined granular geographic level. This research will build on applicable efforts recently documented in NCFRP Report 8: Freight-Demand Modeling to Support Public-Sector Decision Making (Cambridge Systematics and GeoStats 2010), specifically as they pertain to local decision-making needs. In addition, the tour-based commercial vehicle model used in Calgary, Alberta, documented by Kuzmyak (2008) in NCHRP Synthesis of Highway Practice 384: Fore- casting Metropolitan Commercial and Freight Travel, is a useful reference for the types of local data that support freight forecasting efforts on a small geographic scale. Benefits and Expected Outcomes ⢠Identify disparate sources of data that currently exist but are not used for model- ing freight movements; ⢠Develop recommendations for refinement or augmentation of existing public data resources (e.g., FAF, CFS, U.S. Census Bureau) and private data sets (e.g.,
24 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN Transearch) to address gaps in data and enhance or supplement these data sources to support analytic tools on smaller geographic scales; ⢠In conjunction with the efforts described for Sample Research Initiative J, provide guidance to all states and local jurisdictions on acceptable data formats that will facilitate incorporation into local freight models and allow for transferability of data across institutional and jurisdictional boundaries; ⢠Establish methods for local agencies to fill gaps in data and create placeholders for freight data that they may not collect currently but plan to incorporate in future freight forecasting methods; and ⢠Improve the understanding of local freight activity that is not captured accurately in national and regional data sets (including local distribution, touring, and inter- modal transfers). SAMPLE RESEARCH INITIATIVE J Establish, pool, and standardize a portfolio of core freight data sources and data sets that supports planning, programming, and project prioritization. Description This sample research initiative recognizes that varied sources of freight data are used by planners and state DOTs. It also recognizes that the use of freight data for analytic, planning, and decision-making purposes is far more of a hodgepodge than a uniform approach. The research for this initiative is built on the following assumptions: ⢠Flexibility in data sources and analytic methods remains important to individual jurisdictional needs and requirements; ⢠The general myth that private sector freight data are unattainable can be substan- tially debunked through the development of some common or core data sets; ⢠The benefits of this research will be substantially greater than its cost because fewer MPOs and state DOTs will duplicate efforts; and ⢠The development of web-based data resources will be an ideal mechanism for sharing and disseminating data. This research is most timely because government agencies are operating under unprecedented fiscal austerity that will likely become even greater. Data collection is typically viewed as expensive and discretionary. This research in effect becomes an intelligent way of pooling resources nationally rather than continuing a fragmented approach to data collection. The organization of this research must include a wide cross section of private and public sector users and data suppliers in the process. A solid foundation for this effort has already been established through the recently published NCFRP Report 9: Guidance for Developing a Freight Transportation Data Architecture (Quiroga et al. 2011). The recommendations and specifications of that report will serve as the basis of this research effort, specifically with regard to its
25 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN three-tiered approach (single application, intermediate, and holistic) for developing a national freight data architecture. This effort will also include research into exist- ing electronic data interchange protocols and processes used in freight transportation (e.g., ANSI 856 Ship Notice/Manifest). Benefits and Expected Outcomes ⢠Improved and more reliable analytic results; ⢠Greater efficiency and cost-effectiveness of planning and analysis; ⢠Opportunity to overcome some of the perceived barriers related to data availability; ⢠Improved understanding of processes in individual industries and their impact on freight demand at different geographic levels; ⢠Improved understanding of the relationships between businesses and industries; ⢠More comprehensive understanding of issues related to full supply chains (as opposed to discrete freight movements); and ⢠Elimination of costly freight data redundancies. SAMPLE RESEARCH INITIATIVE K Develop procedures for applying freight forecasting to the design of transportation infrastructure, particularly pavement and bridges. Description Freight movements have unique characteristics and infrastructure needs. Often these needs are not fully considered in infrastructure design, particularly with regard to pavement and bridge design. Procedures and processes to integrate true freight fore- casting into this design will ensure that current and near-term projects will not become future freight constraints. Research includes collecting best practices for considering freight needs in the design, construction, operations, and maintenance of roadway infrastructure. The research efforts in this initiative involve documenting the role of truck weight and volumes in pavement and bridge design among public agencies in the United States and abroad, along with the role that forecasting tools play in the design process. Design vehicles, current and projected volumes, and oversized load considerations will be examined. The focus of this effort is to bring freight planning tools and data into the design process for pavement and bridges. The recommended approach for this research includes an initial survey of highway departments and toll authorities, with detailed documentation of how these agencies incorporate truck activity into the design and maintenance of their infrastructure. The primary design parameters to be included in this effort include truck volumes and vehicle weight, length, height, and axle configuration. Procedures and processes to integrate true freight forecasting into this design will ensure that current and near-term projects will not become future freight constraints.
26 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN This research will build on ongoing developments in GPS tracking and asset man- agement for truck fleets, along with existing weigh-in-motion, permit, and routing data. Collaboration with private industry for vehicle configuration and weight data (when possible) is crucial to the success of this effort. Benefits and Expected Outcomes ⢠Documentation of the role of truck size and weight characteristics in the planning, design, and maintenance of highway infrastructure across an array of different agencies; ⢠Identification of design parameters for which changes in future truck activity, mea- sured in terms of increased truck volumes, changes in truck sizes, or changes in load characteristics (i.e., full versus empty trucks), can influence life cycles and design standards; and ⢠Identification of ways to incorporate freight planning tools and data into the plan- ning, design, and maintenance of highway infrastructure. SAMPLE RESEARCH INITIATIVE L Advance research to effectively integrate logistics practices (private sector) with transportation policy, planning, and programming (public sector). Description There is a substantial disconnect between private and public sector decision making related to the movement of goods and the infrastructure that supports those activities. This research builds on the behavior-based freight research documented in Sample Research Initiative C and attempts to integrate the real-world supply chain manage- ment practices of the private sector with the policy and planning decision making of the public sector. Although it is unrealistic to expect that the timelines and planning horizons of the public and private sectors will be fully harmonized in an effective man- ner (public sector planning horizons are typically years or decades in length, but the decision-making needs of private industry can change almost on an hourly basis), a thorough understanding of the decision-making needs of both private and public sec- tors enhances the interests of both. This research is an initiative to determine areas of mutual benefit for improving data and planning tools across public and private sectors and to develop ideas for plan- ning processes to incorporate actual supply chain management processes and logistics decisions to the extent possible. The research includes answers to why, how, when, where, what, who, and how much in order to bridge the gap between how shippers and carriers operate on a short-term basis and what the public sector needs to make decisions, taking into account that decision making often requires years of planning. Recommendations for streamlining public sector decision-making processes are beyond the scope of this effort, but the research will yield interesting ideas about approaches to public sector planning and programming efforts that include phased implementation,
27 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN interim short-term improvements in place of costly long-term investments, and condi- tional approvals. NCHRP Report 594: Guidebook for Integrating Freight into Transportation Plan- ning and Project Selection Processes (Cambridge Systematics et al. 2007) will serve as a foundation and an instructional guide for this sample research initiative. This research includes the following areas: ⢠Real estateâWhat are the standard timelines for private sector investments in real estate that is related to freight activity, including port and rail terminals, dis- tribution centers, and truck terminals and hubs? What are some of the variations among different types of ownership and operating arrangements, including site selection, modifications of existing facilities versus construction of new facilities, and build-to-suit versus âspecâ buildings? ⢠Facility operationsâWhat are some of the ongoing changes in facility operations that extend the useful life of existing facilities or result in dramatic changes in off- site impacts? What are some of the factors that drive functional obsolescence of facilities far in advance of physical depreciation of these assets, and what are the life-cycle implications of these factors? Examples of these include port and rail terminal hours of operation, the gradual transformation of warehouses (focused on product storage) to distribution centers (focused on load consolidation and dis- tribution, with accompanying reductions in on-site product inventory), and other improvements in the efficiency of facility operations. ⢠Vehicles and vesselsâThis research area includes freight rolling stock, such as trucks and railcars, in addition to freight vessels (including barges). How have the dimensions of these various elements of the freight system changed over time, and what are the implications of these changes with regard to roadway design, bridge height and weight limits, channel depth, and other infrastructure considerations? How frequently do these elements of the system change relative to the life cycles of the accompanying infrastructure? What are the life cycles of these vehicles and vessels, and how quickly do different types of carriers change their fleet manage- ment decisions to reflect changing business conditions? ⢠InfrastructureâWhat are the timelines for various types of infrastructure improve- ments, including roadway and rail rehabilitation, new construction of these types of facilities, channel deepening, and other major infrastructure development? What potential changes should be made in the permitting and approval processes for these elements to provide short-term or conditional capacity enhancements to reflect the needs of private industry as documented in the above items? Benefits and Expected Outcomes ⢠Provide an understanding of the investment cycles for different elements of the freight transportation industry, including shippers, carriers, and public agencies, encompassing decision-making cycles for land use, facilities, and infrastructure;
28 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN ⢠Provide a detailed view of operational, rolling stock, and supply chain decisions that may change on a short-term basis to reflect private sector needs; ⢠Document potential inefficiencies in the freight transportation system related to the disparate decision-making cycles of the public and private sectors; and ⢠Develop potential approaches through which public agencies can implement short-term or interim measures to reflect the changing, dynamic needs of the pri- vate sector. SAMPLE RESEARCH INITIATIVE M Develop visualization tools for freight planning and modeling through a two-pronged approach of discovery and addressing known decision-making needs. Description Visualization tools provide a powerful means of communicating complex concepts and data. This research is aimed at providing analysts with tools for managing data (dis- covery) in an organized and intuitive way to make freight information more accessible, understandable, and usable. In addition, this effort seeks to apply visualization tech- niques to provide a sensible platform for developing more robust forecasting models while communicating concepts and analyses to decision makers. A general need for the freight planning and forecasting industry is the enhancement of computer science skills related to graphic presentation. The nature of the freight transportation process across different geographic scales lends itself well to a geographic information system platform for visualization. An innovative element of this research involves developing standard visualization tech- niques and tying them to improved web access to data for different geographic levels. Private companies such as Esri that already develop products for a multitude of clients will be good partners and research champions, along with other private sector firms that develop tools to meet their own internal needs. This initiative includes the use of gaming technologies and methods as part of learning systems to address knowledge and skill deficits in freight planning and model- ing. Existing tools and methods currently being applied in all levels of education (e.g., Epistemic Games at the University of WisconsinâMadison and the Virtual Construc- tion Simulator at Penn State University) are examples of ongoing developments that may be applicable to this field. Benefits and Expected Outcomes ⢠Foster a strong interest among data visualization and user interface experts cur- rently applying their skills in other industries to bring their talents to bear on freight demand modeling and data, to assist this industry with seeing, understand- ing, and communicating; Visualization tools provide a powerful means of communicating complex concepts and data.
29 FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN ⢠Apply visualization tools and techniques systematically across other appropriate research initiatives in this effort to enhance their effectiveness and promote uncon- ventional thinking; ⢠Use visualization as a common language to promote a greater understanding and more productive dialogue among modelers, planners, researchers, the private sec- tor, and other stakeholders; and ⢠Incorporate visualization techniques to evaluate innovative freight demand models.
