Forecasting Statewide Freight Toolkit (2008)

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35 7.1 Introduction Performance-based planning provides a consistent, repeat- able, and transparent process for developing and selecting transportation projects and policies. This section presents a comprehensive list of freight performance measures and tools needed to address states’ primary analytical and policy freight transportation needs. These needs, described in Section 3.0 and summarized in Table 7.1, were identified through tele- phone, Internet, and e-mail surveys targeted primarily to state departments of transportation. The table shows 15 primary analytical and policy areas, which were screened for forecasta- bility and then further screened and matched according to appropriate tool components for calculating the measures. The performance measures were assembled from numerous current sources, then matched to the 15 analytical and policy areas. Sections 7.2 and 7.3 list the freight performance measures that can be forecast and calculated using tool components from the toolbox. Section 7.2 also lists freight performance measures that cannot be forecast or calculated using the toolbox. Section 7.3 matches the tool components to the perform- ance measures. Section 7.4 presents an abbreviated, targeted list of measures for states to use to address their freight trans- portation analytical and policy needs, and describes each measure in detail. 7.2 Performance Measures for States’ Primary Needs Seven different state, Federal, and international sources were used to assemble a comprehensive list of freight-related performance measures: • Cambridge Systematics, Inc. National Transportation System Performance Measures Final Report.U.S. Depart- ment of Transportation, Washington, D.C., April 1996. • Cambridge Systematics, Inc. and David Evans and Associ- ates. ODOT Operations Program Performance Measures Draft Final Report. Oregon Department of Transportation, June 2001. • Cambridge Systematics, Inc. NCHRP 446: A Guidebook for Performance-Based Transportation Planning. TRB, National Research Council, Washington, D.C., 2000. • Performance Measures Summary. Minnesota Department of Transportation, January 1999. • Cambridge Systematics, Inc. Texas Transportation Plan: Objectives and Outcome Measures. PowerPoint presenta- tion by Arlee Reno, April 13, 1999. • Marbek Resource Consultants, Ltd. How Jurisdictions are Measuring Performance of Transportation Policy and Planning. Ministry of Transportation (Canada), October 16, 2001. • Measures, Markers and Mileposts: The Grey Notebook for the Quarter Ending June 30, 2002. Washington State Depart- ment of Transportation, June 30, 2002. The performance measures were first screened for fore- castability. For example, future shipper satisfaction with modal or scheduling flexibility is not something that can be predicted with existing data and tools. Next, the measures were screened based on the available tools in the Toolkit. Thus, while freight dock availability could potentially be fore- cast if detailed data on a facility’s current capacity and usage, as well as future demand, were available, there are currently no tools in the Toolkit to support such an analysis. Table B.2 in Appendix B presents 55 freight-related per- formance measures that are forecastable and can be calculated using available tools. In addition, each of these measures addresses one or more of the freight transportation policy and analytical needs indicated by states. Italicized measures form a short list of recommended measures, and are explained in detail in Section 7.4. Tables 7.2 and 7.3 show performance measures in the context of policy needs and analytical needs, respectively. C H A P T E R 7 Performance Measures

Many more freight-related performance measures are avail- able that are not easily forecastable or cannot be evaluated with the current available tools. However, if a state or agency is will- ing to collect extra data, build a new tool, or simply measure current performance, these additional measures could be use- ful. Table 7.4 presents these additional freight performance measures. Many of the measures evaluate very specific con- cerns, such as the number of docks at a port. Several others are measurements of people’s opinions and perceptions. 7.3 Tools for Measuring Performance Sections 4.0 and 6.0 provide detailed descriptions of five freight model classes and their various components. These classes and components are presented in Table 7.5. Section 4.0 also describes specific models currently being used in each class. The most appropriate method of gathering data for input to the tools or of calculating performance measures is direct, continuous measurement of shipments, vehicles, or facilities (such as, vehicle travel time or average speed at a specific highway location). However, continuously collected data to develop freight performance measures are severely limited, at least currently. Monitoring individual vehicles and cargo on a large scale is problematic due to privacy concerns on the part of carriers and shippers as well as lack of standards for reporting. Roadway surveillance coverage is restricted pri- 36 marily to urban areas, and the practice of archiving these data is not yet widespread. Therefore, while direct measurement and data collection are the desired goals, some degree of model application must be accepted if freight performance measures are to be enacted in the near term. Appendix B presents the tool components required for gen- erating data for each performance measure. Often, multiple tool components can be used for a single measure. For example, to calculate the “average cost per trip,” one could potentially use the data from either the direct factoring of facility flows in a facility flow or O-D flow model, or from network assignment in an O-D flow, truck, four-step commodity flow, or economic activity model. For some performance measures, a tool com- ponent can only be used from a specific class of model. Though the mode split component can be found in O-D factoring, four- step commodity, and economic activity model classes, only mode split results from the four-step commodity class can be used to calculate “number of users of intermodal facilities.” Ital- icized measures form a short list of recommended measures, and are explained in detail in Section 7.4. 7.4 Recommended Toolkit Performance Measures The performance measures listed in Section 7.3 can all be calculated using tools available in the Toolkit and all address one or more of the states’ analytical or policy concerns. This Need Description State Planning State transportation planning including preparation of state multimodal trans- portation plans and/or freight plans. Project Prioritization Project prioritization, statewide transportation improvement plan development. Modal Diversion Modal diversion analysis. Pavement and Safety Pavement, bridge, and safety management. Policy and Economic Policy and economic studies for Governor, legislature, commission, etc. Needs Analysis Needs analysis. Commodity Flow Commodity flow analyses to understand the types, values, and economic importance of freight movement to, from, and within the state. Rail Planning Rail planning. Trade and Border Trade corridor and border planning. Operational Needs Operational needs. Project Development Project development or design needs, e.g., forecasts and loadings. Terminal Access Terminal access planning; forecasting truck loadings for highway access facili- ties to ports, other intermodal terminals, and grain or other heavy commodity terminals. Truck Flows Truck flow analysis and forecasting. Performance Measurement Performance measurement/program evaluation. Bottlenecks Bottleneck analysis. Table 7.1. States’ primary freight policy and analytical needs.

section presents a simplified, abridged list of more targeted performance measures. These measures address all of the analytical and policy areas without overlapping, are easy to measure with available tools, and provide the most mean- ingful information to analysts, decision-makers, and the public. This subset of measures can be used to create com- prehensive performance measurement. They are mostly multimodal, and address both the performance of facilities as well as trips. Table 7.3 and Appendix B show the 17 recommended per- formance measures in italics. By referring to Table 7.3, one can see the analytical and policy areas the measures address. By referring to Appendix B, one can identify the modeling components required to calculate them. Below is a brief description of each recommended measure. • Administrative, Engineering, and Construction Cost/ Ton-Mile (Owner Cost). This measure of operating effi- ciency aids states in policy and economic studies; pavement and safety management; needs analysis; and project priori- tization. It can help a state establish benefit/cost ratios. Ton- miles are derived from either direct factoring of facility flows or network assignment, and each agency generally has its own accepted unit costs. • Average Circuitry for Truck Trips of Selected O-D Pattern. This travel time-based or distance-based measure addresses issues of accessibility and connectivity in truck routes. It can be used for states’ truck flow and project development and design-related needs. Network assign- ment results can be used to find the average truck trip travel time or distance for a selected O-D pattern, which is then compared to an “optimal” time or distance (for example, based on a straight-line distance or an interstate connection between the O-D pair). • Average Fuel Consumption Per Trip for Selected Trips (or Shipments) or Per Ton-Mile. This freight performance measure considers environmental and resource conserva- tion, as well as operating efficiency. It is useful for modal diversion analysis (measuring the environmental and mon- etary costs associated with different modal options) and addressing states’ performance measurement and program evaluation needs. Fuel consumption calculations, generally a function of vehicle type, roadway functional classification, and average speed, can use network assignment results and 37 Policy Needs Performance Measure Average fuel consumption per trip for selected trips (or shipments). Fuel consumption per ton-mile traveled. Market share of international or regional trade by mode. Average cost per trip. Average shipment time, cost, variability in arrival time for freight shipments (local versus long distance, by commodity, by mode). Additional revenue earned by producers when shipping via rail. Modal Diversion Average travel time from facility to destination (by mode). Administrative, engineering and construction cost/ton-mile (owner cost). Economic indicator for goods movement. Freight transport system supply (route miles, capacity miles, number of carriers, number of ports/terminals) per “demand unit” (dollar of manufacturing output, ton-mile of commodity movement, capita, employee, etc.). Miles of freight routes with adequate capacity. Dollar losses due to freight delays. Policy and Economic Mobility index (ton-miles of travel/vehicle-miles of travel times average speed). Project Prioritization Administrative, engineering and construction cost/ton-mile (owner cost). Delay per ton-mile traveled (by mode). Exposure (annual average daily traffic and daily trains) factor for rail crossings.Rail Planning Additional revenue earned by producers when shipping via rail. Trade and Border Market share of international or regional trade by mode. Table 7.2. Policy needs and corresponding performance measures.

standard fuel consumption rates. Also, some post-processors (such as the Intelligent Transportation Systems Deployment Analysis System or IDAS) take highway networks and O-D trip tables as inputs and calculate average fuel consumption for selected areas. System-specific or facility-specific fuel con- sumption can be divided by total trips or total ton-miles to normalize the result for comparison between different sys- tems and facilities. • Average Travel Time from Facility to Major Highway, Rail, or Other Network. Accessibility, mobility, and oper- ating efficiency are all evaluated by this targeted travel time measure that addresses port and intermodal terminal access needs. Assignment results yield average times on the selected facilities. Substandard performance can indicate needs for upgraded facility access infrastructure or system management, or for a new major highway, rail, or other modal link closer to the facility. • Delay Per Ton-Mile Traveled (by Mode). This travel time- based performance measure addresses mobility, and states’ needs for rail planning and modal diversion analysis. Data for calculating this measure can be taken from direct fac- toring of facility flows or network assignment; delay on any facility is the difference between the actual travel time and the free-flow travel time. Dividing delay by total ton-miles 38 Analytical Needs Performance Measure Bottlenecks Frequency of delays at intermodal facilities Average cost per trip. Average shipment time, cost, variability in arrival time for freight shipments (local versus long distance, by commodity, by mode). Commodity Flow Business volume by commodity group. Cost per ton of freight shipped. Cost per ton-mile by mode.Modal Diversion Delay per ton-mile traveled (by mode). Administrative, engineering and construction cost/ton-mile (owner cost). Average crash cost per trip. Dollar losses due to freight delays. Economic indicator for goods movement. Freight transport system supply (route miles, capacity miles, number of carriers, num- ber of ports/terminals) per “demand unit” (dollar of manufacturing output, ton-mile of commodity movement, capita, employee, etc.). Needs Analysis Fuel consumption per ton-mile traveled. Operational Needs Interference of movement at grade crossings – delay time and speed. Administrative, engineering and construction cost/ton-mile (owner cost). Average crash cost per trip. Pavement and Safety Exposure (annual average daily traffic and daily trains) factor for rail crossings. Average fuel consumption per trip for selected trips (or shipments). Performance Measurement Mobility index (ton-miles of travel/vehicle-miles of travel times average speed). Average circuity for truck trips of selected O-D pattern. Project Development Frequency of delays at intermodal facilities. Project Prioritization Dollar losses due to freight delays. Average travel time from facility to destination (by mode). Terminal Access Average travel time from facility to major highway, rail, or other network. Average circuity for truck trips of selected O-D pattern. Average speed (passenger and commercial vehicles) on representative highway segments.Truck Flows Interference of movement at grade crossings – delay time and speed. Table 7.3. Analytical needs and corresponding performance measures.

39 Air cargo carrier route miles. Amount of turning radius from major highway to intermodal facility. Annual percent increase of unit costs of transport industries. Availability of real-time cargo information. Average distance to intermodal terminals from differ- ent community shipping points. Average processing time for shipments at intermodal terminals. Average time between arrival and clearance of hazardous materials spill. Average time between hazardous materials notifica- tion and response. Capacity of intermodal terminals. Capacity of package express carriers. Cost by commodity. Customer perception of time it takes to travel to places people/goods need to go. Customs delays. Delay time at primary commercial airports. Dollar expenditures for freight rail. Dollar value of property loss per ‘X’ users of inter- modal transfer points. Double-stack capacity (or rating). Environmental impacts related to spills of hazardous materials. Freight carrier (or local shippers) appraisal of quality of highway service in terms of travel time/speed, delay, circuity, scheduling convenience. Freight dock availability. Grade crossing safety improvements. Lift capacity (annual volume). Miles of double-stack track. Number of hazardous materials spills. Number of hazardous materials spills per vehicle- mile of hazmat traffic. Number of intermodal facilities that agency assists in development. Number of intermodal terminals by type. Number of marine barge operators. Number of overload permits rejected due to structural capacity deficiency. Number of package express carriers. Number of pipeline spills and accidents. Number of ports with railroad connections. Number of posted bridges and bridge load carrying capacity. Number of registered trucks by type/asset. Number of state-owned navigational aids Number of structures with vertical (or horizontal) clearance less than X feet. Number of 20-foot equivalent units (TEUs, 10’x 20’) (or railroad cars or containers) that can be stored on the premises of the intermodal facility. Number of track-miles abandoned or under threat of abandonment. Number of trucking companies by type. Number of trucks that can be loaded with bulk mate- rial per hour of loading time. Pavement condition on links to intermodal facilities. Percent lane-miles that are truck priority (or excluded) Percent of businesses that cite problems with transportation (access, travel time, cost, flexibility, reliability, damage/losses) as a major factor in productivity or expansion. Percent of commercial vehicles weighed that are over- weight (by fixed and portable scales). Miles of rail-line acquired and rehabilitated for rail service. Miles of roadway not usable by certain traffic because of design or condition deficiencies. Miles of track by Federal Railroad Administration’s speed rating. Miles of track in operation (by Federal Railroad Administration rating). Miles of track not usable by certain traffic because of design or condition deficiencies. Miles of trunk highway with springtime weight restrictions. Number (or percent) of shippers able to access desired suppliers or markets by preferred and secon- dary mode within specified service parameters (e.g., shipment time, cost, circuity). Number of air cargo carriers. Number of airports within X minutes of agricultural centers capable of supporting twin engine piston powered aircraft. Percent of intermodal connecting points and facilities accurately placed on a map. Percent of manufacturers/shippers that have relo- cated for transportation purposes. Percent of railroad grade crossings under electronic surveillance. Percent of road system carrying unrestricted loads year round. Percent of shippers satisfied with access and service to global markets. Percent of truck highway bridges sufficient in load capacity, vertical and horizontal clearance. Posted bridges and bridge load carrying capacity by functional class (number, percent, and area). Public expenditures on modal systems (freight versus passenger). Rail freight revenue versus operating expenses. Railroad/highway at-grade crossings. Route miles served by marine barge operators. Table 7.4. Additional freight performance measures. (continued on next page)

normalizes the measure for comparison among different systems and facilities. Examining delay at a specific location, normalized by total trips passing through that location, is an effective tool for bottleneck analysis. • Dollar Losses Due to Freight Delays. This measure is a func- tion of “delay per ton-mile traveled (by mode)” and ad- dresses mobility and operating efficiency. It can help states with policy and economic studies, project prioritization, and needs analysis. This measure is particularly useful for ex- plaining performance improvements and reductions to ship- pers and carriers. Data for calculating this measure can be taken from direct factoring of facility flows or network as- signment, and then applied to monetized values of time. • Freight Transport System Supply (Route Miles, Capacity Miles, Number of Carriers, Number of Ports/Terminals) Per Demand Unit (Dollar of Manufacturing Output, Ton- Mile of Commodity Movement, Capita, Employee, etc.). This mobility measure more closely examines the supply side of transportation systems, helping states evaluate policy and economic studies and transportation needs. A variety of tool components can be used, depending on the desired demand unit. Any mileage-based unit, such as ton-miles or vehicle- miles, will require direct factoring of facility flows or network assignment. Other economic and production-related units may require an economic model component or a trip gener- ation component based on exogenous data. • Mobility Index (Ton-Miles of Travel/Vehicle-Miles of Travel Times Average Speed). This mobility measure con- siders policy and economic and performance measurement needs. It can be calculated using results from either direct 40 Number of commercial vehicle safety inspections performed. Number of commercial vehicles weighed (by fixed and portable scales). Number of crashes (or injuries or fatalities) caused by waterborne transportation. Number of crashes (or injuries or fatalities) per ‘X’ users of intermodal transfer points. Number of crashes per ton-mile traveled. Number of dockage days at seaports. Number of fatalities and injuries occurring on the rail system. Number of freight railroads by class. Shipper satisfaction with modal/scheduling flexibility. Shipper satisfaction with on-time reliability, shipping costs, or shipping time. Total duration of hazardous materials spill. Track capacity (size, acreage). Track condition. Truck delivery and loading interference with street traffic. Truck turnaround time at intermodal terminals. Shipment processing time at intermodal terminals. Table 7.4. (Continued). Model Component Model Class Direct Factoring Trip Generation Trip Distribution Mode Split Traffic Assignment Economic/Land Use Modeling Facility Factoring Method Of facility flows O-D Factoring Method Of O-D flows Included Included Truck Model Based on ex- ogenously sup- plied zonal activity Included Not Applicable Included Four- Step Commodity Model Based on ex- ogenously sup- plied zonal activity Included Included Included Economic Activity Model Based on out- puts of eco- nomic model Included Included Included Included Table 7.5. Freight model classes and components.

factoring of facility flows or network assignment. The mobility index can be used as a primary marquee measure, with other supporting measures to address other concerns. • Mode Split (by Ton-Mile). Mode split by ton-mile addresses operating efficiency, and aids states in model diversion analysis. It requires mode-split model component results, and to normalize by ton-mile requires data from direct factoring of facility flows or network assignment. • O-D Travel Times (by Mode). This mobility and connec- tivity measure uses network assignment to derive modal travel times between selected O-D pairs and assess modal diversion and truck flow needs. • Percent of Freight Trips Occurring Within Peak Periods. Calculating the percent of freight trips occurring within peak periods measures the operating efficiency within a region. This measure can assist states with their truck flow analysis and operational needs. While most work trips are confined to the peak periods and therefore the highest levels of congestion, freight trips are usually not confined to a particular time of day. Understanding the percent of freight trips occurring during the peak periods can help an agency find policies to shift freight trips to less congested times of day, thereby improving all of the other travel time- based measures for freight. This measure can be calculated using direct factoring of O-D flows and trip distribution tool components. • Percent of Manufacturing Industries Within X Miles of Interstate or Four-Lane Highway. This accessibility meas- ure addresses needs for upgrading highway facilities, build- ing new four-lane or interstate facilities, or changes in land policies. Economic models can be used to forecast this measure. • Percent of Traffic on Regional Highway that is Heavy Truck. High percentages of heavy truck traffic mixed with passenger vehicle traffic can be a concern for both safety and system preservation. This measure addresses states’ pavement and safety management needs and operational needs. Direct factoring of facility flows or network assign- ment can be used to forecast this measure. • Ton-Miles Traveled by Congestion Level. This measure, based on volume-to-capacity (V/C) ratio, measures mo- bility in a transportation system and addresses project pri- oritization and project development and design needs. It examines how often freight must travel in congested con- ditions. Direct factoring of facility flows or network as- signment can be used to calculate this measure. • Tonnage Originating and Terminating. This economic development performance measure helps states evaluate commodity flow issues. Although it does not measure spe- cific or systemwide network problems, or shipment or ve- hicle performance, it can be used as an economic indicator for a region and can help states plan future infrastructure and create appropriate policies in the region. The measure can be calculated from trip generation tool components based on either exogenous data or an economic model. • Truck Vehicle-Miles Traveled by Light Duty, Heavy Duty, and Through Trips. Truck vehicle-miles traveled (VMT), a basic measure of mobility, is useful for state planning and project prioritization. It can easily be obtained from tool components that directly factor facility flows or from net- work assignment. Stratifying the measure by light duty, heavy duty, and through trips helps states understand the nature and purpose of truck trips in a region, as well as pre- dict safety and pavement preservation problems (see “Per- cent of Traffic on Regional Highway that is Heavy Truck”). • Volume-to-Capacity Ratio on Facility Access Roads and at Border Crossings. This targeted measure of mobility and economic development addresses states’ trade and border, terminal access, and bottleneck analysis needs. The volume-to-capacity (V/C) ratio is a common and easy to understand measure of congestion, often conveyed to stakeholders by level of service (LOS) designations. It can be applied to any transportation facility under study. This measure can be forecast from the direct factoring of facil- ity flows or network assignment. 41