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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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Suggested Citation:"Chapter 4 - Guidebook." National Academies of Sciences, Engineering, and Medicine. 2010. Freight-Demand Modeling to Support Public-Sector Decision Making. Washington, DC: The National Academies Press. doi: 10.17226/14445.
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46 The interviews with transportation practitioners found that freight forecasting methods generally are supportive of public decisionmaking. The literature review of existing models sug- gests that, although refinements of processes are in order, par- ticularly for simulation and logistics models that might support operational and public–private investment decisions, the mod- els that do exist can support public decisionmaking. The largest gap in the application of these models that was identified from the interview process was data to support the models. The research topics in Section 3 were selected not only to develop data that might be used to support these models, but also to show that publicly available data or low-cost data acquisitions can be used to develop data that can improve freight forecast- ing models. The review process also has led to an identification of a standard process that could be documented as a guidebook to be followed in the development and application of freight forecasts that can support public decisionmaking. This section is intended to outline the steps that should be followed by practitioners. The discussion is not intended to be exhaustive. These topics have been addressed in detail in other reports such as the QRFM, the National Highway Institute (NHI) course on Freight Forecasting, FHWA’s Accounting for Com- mercial Vehicles in Urban Transportation Models, and NCHRP Report 606: Forecasting Statewide Freight Toolkit. The purpose of this section is to outline the steps to be followed, not to describe the details of the steps. 4.1 Step Outline Step 1. What Freight Policy Alternatives Need to be Evaluated? Forecasting fulfills two purposes. First and most typically, forecasts are prepared to evaluate future conditions. For example the employment in certain industries in a region, which are known to be drivers of freight, might be used in a model that was validated to current conditions to forecast future freight demand and performance. The second purpose of forecasting is to estimate informa- tion that is difficult or costly to determine by direct measure during the current period. For example, information about the commodities carried by truck is not readily available from vehicle classification counts or other observations of general truck performance. However, models that are developed to forecast freight movement by commodity, as well as other nonfreight truck movements, can be used to calculate the per- formance of these various types of flows that otherwise could not be reported. The policy alternatives to be analyzed will dictate the need for forecasts. Some policies are short-term in nature but require a great deal of detail. Some policies are long-term and long-term forecasts of demand and performance are needed, but with less detail. Table 4.1 shows general policy needs that transportation planners are asked to address. Although other policy needs do exist (e.g., project design, safety, infrastruc- ture, and maintenance planning), most often these are subsets of general and long-range planning. These unlisted needs dif- fer only in the precision and amount of detail to be included in the forecasts. • General and long-range planning can require “forecasts” of current conditions, primarily to add details not other- wise available (e.g., performance by commodity flows). The long-range forecasts are, of course, long-term and will require information about economic and other conditions that give rise to freight demand, as well as the future sys- tem that will be serving freight demand (the existing plus committed system). The demand for freight is compared to supply that can carry that freight in order to determine performance. The system is examined to determine where performance is below standard. Projects, programs, and policies are developed to address these needs, and forecasts are prepared with these elements in place. The perfor- C H A P T E R 4 Guidebook

mance of the system with various scenarios (which are combinations of projects, programs, and policies) in place is used to evaluate these scenarios. The forecasting method must be robust enough to develop useful information for this policy analysis. • Project prioritization generally has the same require- ments and issues as general and long-range planning. It does require more precision than general and long-range planning because it is intended to allow the ranking and scheduling of the projects identified during long-range planning. It will require performance by project, rather than systemwide. It is listed separately because it may be a separate focus to address certain emphasis areas and/or legal requirements. • Modal diversion also generally has the same requirements and issues as general and long-range planning. It may require more detail for certain corridors and/or geography. The forecasting process itself may not be different, but the detail of the output and the manner in which it is presented may be different. It is listed as a separate need because it may have a separate policy and/or legal focus. Additionally, general plans traditionally are developed for the trans- portation system owned, maintained, and operated by the public. For freight, this is primarily the highway system. Modal diversion as a policy alternative may address how much freight will be expected on the highway system by shifting demand to alternative modes not the focus of tra- ditional highway-oriented planning. It also is listed sepa- rately because modal diversion of freight is often of inter- est when reviewing energy and environmental policies, such as the emissions by freight, the energy required to move freight and the greenhouse gases associated with the movement of freight. • Policy and economic needs, like the preceding needs, also can be considered as a subset of general and long-range planning. These analyses may require more detail by corri- dor or geography. The forecasting process itself may not be different, but the detail and manner in which it is presented is different and additional processing may be required. The focus of these policy alternatives will be those projects and programs that, by improving the capacity or operations of the freight system, create new economic activity or expand or retain the existing economic activities in a region. It is listed separately because it may be the focus of separate pol- icy and/or legislative requirements. • Rail planning also generally has forecasting needs that are similar to those of general and long-range planning. It is listed separately because it may be the focus of legislative or administrative actions and funding. It differs from other freight issues in that the infrastructure supporting rail freight is generally privately owned and therefore it may be necessary to report separately on public and private demand and performance. These policy analyses may also support the specialized needs and precision required of public– private partnership funding agreements. Defining the freight polices that need to be evaluated is the first step in identifying the appropriate freight forecasting procedures that should be followed. Step 2. What Performance Measures Support Those Policy Measures? As transportation planning and operating agencies strive to improve their efficiency and effectiveness, they have increas- ingly turned to performance measures to provide credible, quantitative information to support their analysis and deci- sionmaking. Measurement of transportation system condi- tion and performance has become an explicitly acknowledged component, not only of the planning process, but also in pro- gramming, budgeting, and system operation. Measures help agencies provide accountability to the public, stay focused on intended results, improve communication with internal and external customers, and improve delivery of services. This is 47 Need Description General and Long-Range Planning Transportation planning including preparation of multimodal transportation plans and/or freight plans; includes forecasts in support of design, asset management, safety, operations, financial planning, and all transportation agency needs Project Prioritization Project prioritization and transportation improvement plan development Modal Diversion Modal diversion analysis Policy and Economic Policy and economic studies Rail Planning Rail planning Table 4.1. Policy needs.

true not only for general transportation, but also for freight- specific policies, programs, and projects. Table 4.2 shows freight performance measures that might be used to support the policy needs identified in Step 1. Gen- eral and long-range planning needs and their related per- formance measures and required forecasting outputs are not shown in Table 4.2 because they include all of the needs and performance measures that are listed. Although the calcula- tion of performance measures will require additional infor- mation that will not be available from the forecasting process, such as the administrative, operating, and construction costs associated with a policy, the forecasting outputs are needed to compute the value of these performance measures. Practi- tioners should consult other documents on the use of per- formance measures. The intent here is to show which fore- casting outputs are required to support the calculation of performance measures. Generally, the performance measure will require detail on the link or system volumes, and the link or system average speeds or times. From these forecasts of demand and performance, practitioners can calculate the performance measurements needed to support the analysis of freight policy alternatives. Step 3. What Forecasting Models can be Used to Support Decisions? As shown in Table 2.1, a framework was developed to organ- ize the literature review and to examine how different classes of models have been implemented to support public decision- making. As the figure shows, only certain classes of models have been found useful and applied in support of public deci- sionmaking. After identifying the freight outputs that are 48 Policy Needs Performance Measures Forecasting Outputs Required Average fuel consumption per trip for selected trips (or shipments) Modal link volumes, modal link speeds Fuel consumption per ton-mile traveled Modal link volumes, modal link speed Market share of international or regional trade by mode Total modal volumes Average cost per trip Modal link volumes, modal link speeds Average shipment time, cost, variability in arrival time for freight shipments (local versus long-distance by commodity, by mode) Modal link volumes, modal link speeds Additional revenue earned by producers when shipping via rail Modal link volumes, modal link speeds Modal Diversion Average travel time from facility to destination, by mode Modal link volumes, modal link speeds Administrative, engineering, and construction cost per ton-mile (owner cost) Modal link volumes, modal link speeds 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.) Modal link volumes Miles of freight routes with adequate capacity Modal link volumes, modal link speeds Dollar losses due to freight delays Modal link volumes, modal link speeds Policy and Economic Mobility index (ton-miles of travel/vehicle- miles of travel times average speed) Modal link volumes, modal link speeds Project Prioritization Administrative, engineering, and construction cost per ton-mile (owner cost) Modal link volumes, modal link speeds Delay per ton-mile traveled (by mode) Modal link volumes, modal link speeds Exposure (annual average daily traffic and daily trains) factor for rail crossings Rail link volumes Rail Planning Additional revenue earned by producers when shipping via rail Modal link volumes, modal link speeds Table 4.2. Policy needs and corresponding performance measures.

required of the forecasting models, it was decided that it was useful to present different categories that should be considered to identify which models will best support the calculation of the performance measures. It is useful to consider the selection of the forecasting process from each of the following groupings: model perspective, model types, and model components. Model Perspective Although freight is the movement of cargo in vehicles, it makes a considerable difference in developing models to fore- cast freight whether those models are being developed from the perspective of the cargo or the perspective of the truck. Fig- ure 4.1 shows a very simple situation of six stops for the move- ment of four cargo shipments. From the perspective of the cargo, there are four productions (two productions at Stop 0— the base, and one each at Stop 1 and Stop 2) and four attrac- tions (one attraction each at Stops 2, 3, 4, and 5). From the per- spective of the truck as a vehicle, there are six productions and six attractions (one each at Stop 0—the base—and Stops 1 through 5). There are five cargo trips as shown by the dotted lines, while there are six truck trips, as shown by the solid lines. Obviously very different models would be required to forecast these movements. This would depend on whether the model was developed to forecast cargo or trucks. This situation for a single truck movement is magnified and compounded when all of the freight shipments within a study area are considered. Model Types Although the model categories in Table 2.1 are useful for cataloging model research, an alternate method of classifica- tion is presented based on how the models are applied. It is a variation of the methods in Chapter 6 of NCHRP Report 606: Forecasting Statewide Freight Toolkit. The model types can best be considered as alternate pathways that follow the steps shown in Figure 4.2. • Trend analysis—This consists only of Step 9a as shown in Figure 4.2. It directly forecasts freight activity using, at most, historical or economic trends. • Commodity forecasting—synthetic modeling of com- modity flows—This consists of Steps 4, 5, and 6, which are used to develop modal commodity flow trip tables, and Steps, 7, 9, and 10, which are used to convert that commod- ity trip table to a suitable format for assignment to modal networks and then to evaluate the flows on those networks. • Commodity forecasting—direct acquisitions of com- modity flows—This consists of Step 6a, which directly acquires a commodity flow table instead of following the synthetic process. If the acquired table includes modal flows and these are directly used, this may replace Step 6. If not, Step 6 is required. After the modal commodity table is obtained, Steps 7, 9, and 10 are followed as in the syn- thetic model. • Economic forecasting—This consists primarily of the feed- back loop between networks perforce and economic inputs shown as Step 6b. Depending on the nature of the economic model, it may have commodity trip tables that can replace some or all of Steps 4, 5, and 6. If the zonal structure in the economic model is different than that used in transporta- tion planning, some conversion may be necessary. • Nonfreight trucks—synthetic modeling—This is shown as Step 8. If a multiclass assignment of highways is used in Step 9, this is a required step and will be necessary to deter- mine the correct multiclass highway performance for freight trucks. If not included, freight performance in Steps 9 and 10 will not consider the interaction with what may be the majority of trucks on the road. It also is possible that Step 8 and Steps 4 through 7 are not followed and that 49 1 2 3 4 5 A Vehicle Trip An O-to-D Commodity Flow Example of a five stop pickup and drop truck trip chain involving four origin-to- destination commodity flows Base of Operations Figure 4.1. Illustrative Freight Shipments Cargo and Truck Perspectives.

commodity freight trucks are included with all trucks in Step 8. If this is the case, the performance of freight trucks cannot be separated from the performance of all trucks. Model Flow Components This dimension is intended to capture how the flow vari- ables are defined in the model steps. The same flow compo- nents need not be in each step. In fact, Step 7 (which is where factors are used to convert from annual tons as flow units in Steps 4 to 6 to daily truck) is excluded to account for this. However, the flow units can be disaggregated based on the need for those units to fulfill specification and calibration rea- sons, model validation requirements, or benefits analyses requirements. • Behavioral, calibration, and specification classifications are developed during model specification and calibration. They are intended to develop forecasting methods and equations for flows with similar behavior. The freight OD flow tables, either produced synthetically or acquired, generally will have separate tables by commodity. This is because com- modities are expected to behave in similar fashion in trip generation to changes in activity drivers, such as employ- ment; in trip distribution to changes in accessibility such as interzonal composite costs; and in mode choice to changes in costs by mode regardless of location. For service or non- freight trucks, this may mean difference by land use cate- gories, since truck trips to, from, and between land uses should behave in a similar manner. • Validations are classifications that are developed to assist in model validation. These may not be flows that can be expected to behave similarly, but reflect flows that are con- sistent with observable characteristics. Thus, while not all single-unit trucks or combination tractor-trailer trucks might be expected to behave in the same manner, this clas- sification may be used in the model because it develops vol- umes that assist in model validation against observed truck classification counts. • Benefits are classifications developed during benefit calcu- lations and may reflect classifications that are useful in benefit/impact analysis. While flow in these classifications will not necessarily behave similarly nor be consistent with observable validation flows, they may be useful classifi- cations in the benefits/impacts calculation. An example would be the use of gross vehicle weight (GVW) for trucks. This may be useful in that different emission rates have been established for different gross vehicle weights, despite the fact that vehicles that have the same GVW are not expected to behave similarly, and that GVW is not a read- ily observable characteristic of truck flow on specific high- way links. 50 Economic Inputs Step 4 Trip Generation Step 5 Trip Distribution Step 6 Mode Choice Step 7 Payload and Temporal Factors Step 8 Service (nonfreight) trucks Step 9 Modal Assignment Step 10 Benefits Analysis Step 6a Acquired Commodity Flow Tables Multimodal By Mode Step 6b Economic Modeling Step 9a Trend Analysis Figure 4.2. Model methods.

Step 4. How Much Freight? Trip Generation: Productions and Attractions by Commodity in Tons This step is necessary for those models that estimate com- modity freight tables synthetically. The volume of commodity flows that begin in a zone, called productions, and an end in a zone, called attractions, must be determined for each zone. Since mode choice is a later step, at this point, the freight flow must be expressed in units that are common to all modes. Commonly, this is tons, although other multimodal units (e.g., value) can be used. To calculate the productions and attrac- tions for each zone, the economic drivers of freight must be available. These drivers will be some indication of the size (e.g., as indicated by employment) of the different industries that produce or attract (consume) freight. Since shipments of com- modities can be expected to be associated with different indus- tries, equations relating the freight productions and attractions will be developed for those commodities that are expected to respond similarly to certain industries. Public agencies gener- ally develop equations for their own study area from a com- modity flow survey for their area. Some general equations have been developed for an FHWA project to disaggregate FAF2 data from regions to counties.12 A sample of these equations is shown in Table 4.3. However, any average equations should be used with caution, since the economies of each state and region are so different that average equations developed for average economic conditions can not be expected to apply. Additionally, equations for freight productions and attrac- tions can not be expected to apply to all zones. In passenger forecasting, there are zones that generate significant trips (e.g., airports) not related to employment as an indicator of activity. These zones are treated as special generators where the num- ber of productions and attractions are directly specified in any model forecasts. In freight forecasting, this same treatment as special generators is required for ports, rail terminals, and 51 12 Cambridge Systematics, Development of a Computerized Method to Subdivide the FAF2 Regional Commodity OD Data to County-Level OD Data, FHWA, January 2009, unpublished report. SCTG NAICS Description Coefficient T-Stat R2 311 Food Manufacturing 0.407 5.11 0.48 Cereal Grains (2) Farm Acres (in Thousands) 0.441 4.20 311 Food Manufacturing 0.188 10.43 0.65 Other Agriculture Products (3) Farm Acres (In Thousands) 0.051 2.14 Meat/Seafood (5) 311 Food Manufacturing 0.053 25.94 0.86 Milled Grain Products (6) 311 Food Manufacturing 0.053 13.64 0.62 113 Forestry and Logging 0.323 4.02 0.70 115 Support Activities for Agriculture and Forestry 0.843 3.91 Logs (25) 321 Wood Product Manufacturing 0.465 6.48 Wood Products (26) 321 Wood Product Manufacturing 0.625 18.37 0.75 113 Forestry and Logging 0.887 13.59 0.73 Newsprint/Paper (27) 323 Printing and Related Activities 0.086 7.38 322 Paper Manufacturing 0.101 10.76 0.81 Paper Articles (28) 323 Printing and Related Activities 0.038 4.82 331 Primary Metal Manufacturing 0.424 8.69 0.75 Base Metals (32) 333 Machinery Manufacturing 0.085 3.24 Articles of Base Metals (33) 332 Fabricated Metal Product Manufacturing 0.115 14.51 0.65 332 Fabricated Metal Product Manufacturing 0.085 2.92 0.63 Machinery (34) 333 Machinery Manufacturing 0.081 2.01 333 Machinery Manufacturing 0.02 3.00 334 Computer and Electronic Product Manufacturing 0.012 4.35 0.70 Electronic and Electrical (35) 335 Electrical Equipment, Appliance, and Component Manufacturing 0.029 2.44 Source: Cambridge Systematics, Development of a Computerized Method to Subdivide the FAF2 Regional Commodity OD Data to County-Level OD Data, FHWA, January 2009, unpublished. Table 4.3. Tonnage production equations for selected commodities (2002, ktons).

other locations that might be significant producers or attrac- tors of freight for commodities, but for where there is no sig- nificant employment in these zones in the industries associated with those commodities. Step 5. Where Does the Freight Go? Trip Distribution: Trip Table Os and Ds This step is necessary for those models that estimate com- modity freight tables synthetically. The distribution of produc- tions from, and attractions to, zones, as calculated in Step 4, must be distributed between all of the zones. Although this dis- tribution may be based on an existing table of freight flows, through a Fratar process, the most common means of synthet- ically distributing trips between zones is through the use of a gravity model. In the gravity model for freight, as in other transportation applications, the mathematical equations used are applied separately for flows with similar behavior (e.g., commodities). The productions and attractions by commod- ity are distributed in the gravity model based on the accessibil- ity between the zones, as measured by the impedance between zones. For freight models, the impedance variable for the large geographies considered by freight is most often found to be distance. By examining the commodity flow survey data, it is possible to determine those parameters, such as the average trip length by commodity, which are used to vary the accessi- bility in response to changes in the impedance variable. The match of the trip length distribution for one commodity in the Florida freight model of the observed commodity flow and the estimated flow in a gravity model is shown in Figure 4.3. It is possible that impedance variables other than distance and other distributions may better match observed data. Practi- tioners are urged to consult freight references such as the QRFM to explore this topic in detail. Step 6. What Mode Does Freight Use? Mode Choice: Trip Table Os and Ds by Mode This step is necessary for those models that estimate com- modity freight tables synthetically. The multimodal tonnages moving between zones must be allocated to the various modes that are used to transport freight. The choice of mode used by freight is a complicated process. As discussed in Section 3.5, the choice will be based on many considerations, including the characteristics of the mode, goods, production zone, and attraction zone. When insufficient detail exists to properly model this choice, either because the format and parameters of the choice equations or the data on the characteristics are not known for the base or forecast year, the future choice of mode is assumed to be the same as the existing choice of mode. When this model of forecasting mode choice is used, as it is in many freight models and in FHWA’s FAF, it is assumed that the factors effecting mode choice are captured in the existing observed mode choice by commodity. Thus, when the mode share is forecast to change over time, it reflects changes in the volume and mix of commodities car- ried. For example, in Table 4.4, which is from the FAF2 state profile of California, the freight mode share by truck is fore- cast to change from 73 percent in 2002 to 77 percent in 2035. However, this is because the forecast of the commodity mix for California is different from the mix in the base year. A basic assumption in the FAF2 is that for each commodity in the FAF2, the mode share in 2035 is the same as it was in 2002. If the mode share is available for an existing year, that table of mode shares by commodity can still be examined to find OD pairs that perform worse than other OD pairs at the same dis- tance. The mode shares for these markets can be adjusted in a qualitative process to reflect policy changes—for example those that might be expected to bring their mode share to aver- 52 Miles Percent 10 9 8 7 6 5 4 3 2 1 0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Observed Estimated Figure 4.3. Florida freight gravity model results (food products).

age conditions. This accepted forecasting technique for mode choice is often referred to as Market Segmentation. In some cases, it is desirable to develop estimates of mode choice for markets in which the modal information is limited (e.g., because the mode was never offered). In this case, adjust- ing the mode share to match observed averages would not apply. For this case and for any case where estimates prepared by a qualitative process are needed, a mode choice model may be developed. This is not a trivial undertaking and will likely require a survey to identify the significant variables in the mode-choice decisions, as well as the coefficients and other parameters that should be applied to these variables. Section 3.5 describes a process where the FAF2 commodity flow was used as an RP survey to determine these variables and their parameters. Section 3.5 did find that, consistent with the choice decisions reveled by the FAF2, that modal distance, which pre- sumably serves as a surrogate for modal cost and time, is the most significant explanatory variable in mode choice. Step 6a. Direct Acquisition of Commodity OD Tables: Alternate Ways to Get Freight OD Tables The equations required in Steps 4, 5, and 6 to develop a synthetic freight trip table by mode most likely will be devel- oped from a commodity flow survey. Typically, those freight surveys, unlike household surveys used in passenger plan- ning, already have been expanded to represent all geographies over an extended time period, most often a year. Although these commodity flow surveys may not be formatted like the trip tables used in freight forecasting, it requires little effort to reformat these surveys into tables. When a commodity flow survey has been acquired and developing the trip generation, trip distribution, and mode-choice equations from that sur- vey, as well as the forecast data required to use these models, is costly, a decision to use that commodity flow directly in the modeling process may be quite reasonable. This step uses an acquired commodity flow survey as a trip table. Generally, this survey will dictate the behavioral classifi- cations used in the model (e.g., the commodities and/or modes in the survey will be used in the forecasting model). Additional processing of the acquired table may be necessary to convert from the geographies used in the survey to the zones used in transportation modeling. Step 6b. Economic/Land Use Model: Alternate Ways to Get Freight OD Table by Mode The process described produces a trip table where the economic/land use activities that give rise to freight are exoge- nously supplied to the freight model. However, the freight demand may be considered as part of a complex iterative economic/land use decision. This step may consist of the rep- etition of earlier steps, which allows the forecast of economic activities to be varied in a feedback loop, after the perform- ance of the system is calculated in Step 10. More complex economic models may explicitly include a trip generation step (freight produced by zones), a trip distri- bution step (freight moving between zones), and a mode- choice step (freight moving between zones), including mode used. Any or all of these steps may replace the synthetic steps described in Steps 4 through 6. The economic model may not use the same geographies as the transportation process, there- fore, it may be necessary to disaggregate flows of the geogra- phies in the economic model to zones compatible with net- work assignment. Unless the economic model includes other nonfreight flows that impact freight performance, there may 53 2002 2035 From State From State Number Percent Number Percent Total 127.4 100 476.9 100 Truck 92.8 73 366.0 77 Rail 11.7 9 35.4 7 Water 1.2 1 2.2 <1 Air, air and truck 0.4 <1 2.6 <1 Truck and rail 4.0 3 14.3 3 Other intermodal 5.0 4 29.5 6 Pipeline and unknown 12.4 10 26.7 6 Source: FHWA, FAF2 California State Profile. Table 4.4. FAF freight shipments from California by weight (2002 and 2035, millions of tons).

be a need to examine the outputs of the economic model with outputs of assignment (e.g., to see if the speeds and times are consistent), and iterate as necessary. Step 7. How Many Freight Trucks? Payload and Temporal Factors: Trip Table Os and Ds by Mode by Vehicle This step converts commodity flow trip tables to a format that is consistent with the assignment process to be used in Step 9. Commodity flows tables, whether acquired or produced syn- thetically, are most often in tons per year. Most transportation assignment processes assign vehicles per day. Thus, it is neces- sary to convert the flow in tons to flow in vehicles (e.g., trucks), and to convert from flow per year to flow per day. The devel- opment of factors to convert tons to vehicles for trucks may be based on local observations or surveys, or may make due with national surveys such as the Vehicle Inventory and Usage Survey. The payload factors, tons per truck, must match the behavioral commodity classification system used by the model. Table 4.5 shows a table of payload factors that is used by Ten- nessee DOT (TnDOT) in freight forecasting. In addition to commodity as shown in Table 4.6, other considerations may be important in developing payload fac- tors. These considerations include the length of the haul, the empty mileage, the class of the vehicles, etc. A second conversion is necessary to the commodity flows to correct for the time period to daily. While other practition- ers have used conversion factors from 250 to 365 days per year, as discussed in Section 3.2, dividing annual flows by 310 days might be the appropriate adjustment for an average weekday. If the average weekday in the forecasting model should be for midweek truck flows, it may be appropriate to divide annual flows by 295 days. In addition to adjustments to average weekdays, commod- ity flow forecast adjustments for seasonal variations may be required. As discussed in Section 3.4, while local commodity flows may vary due to local facilities and conditions, national averages indicate little need to adjust average commodity flows for seasonal variations. For some applications, it may be necessary to adjust com- modity truck flows to hourly volumes. Again based on the research reported in Section 3.4, the hourly flows for trucks should be considered to be 6 percent of daily flow for each of the hours from 11:00 A.M. to 7:00 P.M. Step 8. What Service and Other Trucks Must be Considered with Freight? Nonfreight Vehicle OD Tables This step supplies a table of all other truck activities, which are in addition to the truck table forecast to carry freight. The trucks that provide services, move construction materials and equipment, and are used in maintenance activities, as well as the local movement of goods that is not included in com- modity flow tables interact with commodity trucks on the highway system. In order to properly determine the perfor- mance of the freight trucks, it is necessary to have tables for all vehicles sharing the highway system with freight trucks, including those trucks that do not carry freight. Freight may move over national distances, and the model area used in forecasting freight flows may not be the same as the model area needed to address nonfreight, service, trucks that have primarily a local area of operation. For that reason mod- els may choose to handle the nonfreight truck table differently than the freight truck table. The forecast of nonfreight trucks will most often be through a synthetic process of trip genera- tion and trip distribution, similar to the steps for freight 54 Commodity Pounds per Truck Tons per Truck Agriculture 48,500 24 Chemicals 48,500 24 Construction and mining 50,500 25 Food and kindred products 48,500 24 Household goods and other manufactures 38,500 19 Machinery 36,500 18 Mixed misc. shipments, warehouse and rail intermodal drayage, secondary traffic 36,500 18 Paper products 46,500 23 Primary metal 51,500 26 Timber and lumber 53,000 27 Source: PBS&J, Tennessee Long-Range Transportation Plan Freight Model, 2005. Table 4.5. TnDOT freight model truck payload after adjustment.

Truck Type 14,000–28,000 Lbs 8,000–28,000 Lbs 14,000–28,000 Lbs 2–4 Axles, 6+ Tire, Single–Unit, 16,000–52,000 Lbs Land Use NWRG Survey (Production) NWRG Survey (Attraction) NCHRP 298 (MAG) NCHRP 298 (SCAG) PSRC Truck Model (Production) PSRC Truck Model (Attraction) Households 0.011 0.011 0.069 0.0087 0.0163 0.0283 Ag/Mining/Construction 0.040 0.044 0.106 0.0836 0.0404 0.2081 Mining 0.0404 10.8831 Construction 0.0453 0.0644 Retail 0.032 0.035 0.132 0.0962 0.0744 0.0090 Government Education/Government 0.037 0.038 0.006 0.0022 0.0135 0.0118 Finance/Insurance/Real Estate 0.008 0.008 0.021 – 0.0197 0.0276 Manufacturing Products 0.050 0.050 0.100 0.0575 0.0390 0.0396 Equipments 0.0390 0.0396 Transportation/Utility 0.168 0.170 0.106 0.4570 0.0944 0.0733 Wholesale 0.192 0.190 0.106 0.0650 0.1159 0.0258 Other – – 0.106 0.0141 – – Source: Cambridge Systematics, SCAG Heavy-Duty Truck Model Update, Southern California Council of Governments, April 2008. Table 4.6. Comparison of trip rates by truck type and land use.

described in Steps 4 and 5 above. The trip generation rates and the trip distribution factors should be developed through the use of commercial vehicle surveys. One example of trip rates for nonfreight trucks is shown in Table 4.6. This table, develop for the Southern California Association of Governments (SCAG), shows rates from other models in order to provide context for the SCAD model development. The development of a non- freight truck trip table may be an adaptation of an existing total truck table. If this is the case care must be taken to avoid dou- ble counting the trucks that carry freight. It will be necessary to adjust the total truck trip rates and distributions to account for the freight trucks that are being handled separately. Step 9. What Facilities Do Freight Vehicles Use? Assignment of Modal Vehicles to Networks This step assigns the freight trip tables, expressed in modal vehicles, to the modal networks. Although public agencies tra- ditionally forecast assignment to the highway system, they less often forecast assignment to other modal networks. That does not mean that Steps 4, 5, and 6 or the alternate processes described above are not worthwhile. Unless freight is addressed multimodally, the trip table of freight trucks could consider all of the multimodal decisions made in moving freight. The dif- ficulty in making assignments to modal networks is twofold. First the information about the other modal networks may be limited. The connections, availability, and capacity of the links forming the other modal networks may not be readily avail- able, particularly in a format that can be used in assignment. When the modal networks are available in a format that can be used in assignment models (e.g., all nodal connectivity issues have been addressed and zonal connectors have been added), the whole issue about how these routing decisions are made must be decided. The routing decisions of freight over the rail- road, air, and water networks reflects business decisions that are in no way similar to the multiclass user equilibrium assign- ment routines used by highways. For the TnDOT and the Association of American Railroads (AAR), rail assignments have been prepared that assign rail trip tables using shortest distance assignments that do not consider operational or capacity diversions. When truck freight assignments are made to the highway system, it must be recognized that freight trucks are not the only vehicles, much less the only trucks, using the road. The performance of freight trucks on the highway network should consider the assignment of the freight truck table, together with the nonfreight trucks discussed in Step 8, as well as all other vehicles, such as autos that use the highway. These multiclass user equilibrium highway assignments already are customarily being prepared by transportation practitioners in support of public decisionmaking. For freight planning, it is also necessary to track the assignment of freight trucks in order to report on their specific volumes and the paths that they use. Step 9a. What Facilities Do Freight Vehicles Use? Direct Estimation This step bypasses all of the forecasting steps described above. It uses the time series models, which consider historical freight flows separately or with other economic factors, to develop freight forecasts. Because those steps are skipped, changes in freight trip generation unrelated to the facility being examined can not be considered, nor can issues of redistribu- tion of freight, modal diversion of freight, or route diversion of freight, which would be explicitly considered by the other steps discussed previously. However, there are instances where a freight model does not include the freight facility for which forecasts are desired. The decision being considered may not be unique enough to warrant the development of a freight model. Impacts of the freight project on other conditions, for example, on the econ- omy or the environmental, may be simple or small enough to be ignored. If this is the situation, the forecasts are limited to a single freight facility, the other impacts are not considered, and the impacts of the project can be considered simply—a trend forecast may be sufficient as a freight forecast. Step 10. How Do Freight Vehicles Perform on the Network? Estimation of Benefits Public decisions are not easily made using the outputs of transportation forecasting models. Public decisions are based on how the scenarios examined produce benefits for the users, business, and society. The benefits, cost, and impacts of trans- portation projects need to be evaluated not only against other transportation projects, but against other public investments and policy decisions. To make these comparisons, it is cus- tomary to process the outputs of transportation models—the volumes and performance of vehicles on modal networks— into other more generic impacts such as direct and indirect costs, emissions of green house gases and other pollutants, and economic development. This step considers the use of models to calculate the bene- fits and impacts of freight transportation projects, policies, and programs. Most benefits evaluations recognize that transporta- tion activities, including freight, can impact the system in mul- tiple ways. For example, Moving Cooler,13 calculates emission impacts from transportation as being related to changes in demand, operation, vehicle technology, and fuel. The freight forecasting steps described above can not consider vehicle 56 13 Cambridge Systematics, Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions, Urban Land Institute, July 2009.

technology or fuel, nor would freight policies be expected to change these elements. However, the steps described above directly consider the changes in demand and operations, as measured by speeds and times. Benefits and impacts are calculated based on using the demand and operational performance associated with freight projects, programs, and policies. The outputs of these evalu- ations may be monetized, may be in environmental emis- sions, or in other units that may allow for useful comparisons. The benefits models may be simple spreadsheet formulations or complicated evaluation packages such as FHWA’s IDAS or STEAM software programs. The interaction with the freight forecasts is to process the outputs of freight forecasting into a format that can be used as inputs into these evaluation mod- els. This may require geographic or temporal aggregations of the outputs for the freight forecasting process. The output of this step is typically used in proving the values for the per- formance measures that were previously described in Step 2. 4.2 New Methods to Generate Freight Demand and Performance Although the freight forecasting process described in the steps above can adequately support most existing public deci- sions, it is not clear that they will always be able to provide this support as the decisions under consideration change. New methods of monitoring, regulating, and charging for vehicle operations may require different models. For passenger activ- ities, this has led to the development of activity-based model- ing, where trips are not considered in isolation but are consid- ered as a chain of trips supporting those activities. A variety of research is underway to study how freight and truck activities would function as activity/chaining models. In order to sup- port these activities for truck and freight models, additional research is needed to determine the number of trips in a chain, the length of trips in the chain, and the degree to which the next stop is governed by the characteristics of the current stop. Section 3.3 described research that was conducted using inexpensive GPS data to determine this information. The records of trucks subscribing to GPS services were examined and processed in several metropolitan areas. Values were pro- duced that can be used in truck chaining models. Truck GPS subscription records can be expected to become more com- monly available. Ways to disclose more detailed information about the type of truck without disclosing proprietary infor- mation are likely to be developed. This may preclude the need for expensive and time-consuming survey efforts and may make truck freight chaining models more widely available. In addition to truck activity chaining in freight, the partner- ship between public and private freight interests is likely to require improved models for the total logistics process. These models are necessary for the public and private decisionmak- ers to have adequate information to determine the value of public–private partnerships. Similarly, some network and facility design models sup- porting freight, which are primarily used to support private investment decisions, may need to be available in public forms if decisions on the value of public–private partnerships to develop these facilities are to be considered. The outreach to public decisionmakers identified the lack of data as a serious gap in preparing freight forecasts. Improve- ments in freight data collection were not a focus of this project because this is being pursued by TRB and U.S. DOT as well as other agencies in many on-going research projects. However, the data that currently are available may be better utilized to prepare the necessary freight-related data. The research topics investigated in Section 3 were specifically chosen to exploit existing public, available—or in the case of subscription GPS— low-cost data. That research has shown that, in addition to new and improved data collection activities, efforts to better use existing data could help fill freight data gaps. 57

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TRB’s National Cooperative Freight Research Program (NCFRP) Report 8: Freight-Demand Modeling to Support Public-Sector Decision Making explores possible improvements in freight demand models and other analysis tools and includes a guidebook to assist model developers in implementing these improvements.

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