Click for next page ( 47


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

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

OCR for page 46
47 Table 4.1. Policy needs. Need Description Transportation planning including preparation of multimodal General and Long-Range Planning 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 mance of the system with various scenarios (which are different, but the detail and manner in which it is presented combinations of projects, programs, and policies) in place is different and additional processing may be required. The is used to evaluate these scenarios. The forecasting method focus of these policy alternatives will be those projects and must be robust enough to develop useful information for programs that, by improving the capacity or operations of this policy analysis. the freight system, create new economic activity or expand Project prioritization generally has the same require- or retain the existing economic activities in a region. It is ments and issues as general and long-range planning. It listed separately because it may be the focus of separate pol- does require more precision than general and long-range icy and/or legislative requirements. planning because it is intended to allow the ranking and Rail planning also generally has forecasting needs that are scheduling of the projects identified during long-range similar to those of general and long-range planning. It is planning. It will require performance by project, rather listed separately because it may be the focus of legislative or than systemwide. It is listed separately because it may be a administrative actions and funding. It differs from other separate focus to address certain emphasis areas and/or freight issues in that the infrastructure supporting rail legal requirements. freight is generally privately owned and therefore it may be Modal diversion also generally has the same requirements necessary to report separately on public and private demand and issues as general and long-range planning. It may and performance. These policy analyses may also support require more detail for certain corridors and/or geography. the specialized needs and precision required of public The forecasting process itself may not be different, but the private partnership funding agreements. detail of the output and the manner in which it is presented may be different. It is listed as a separate need because it Defining the freight polices that need to be evaluated is the may have a separate policy and/or legal focus. Additionally, first step in identifying the appropriate freight forecasting general plans traditionally are developed for the trans- procedures that should be followed. portation system owned, maintained, and operated by the public. For freight, this is primarily the highway system. Step 2. What Performance Measures Modal diversion as a policy alternative may address how Support Those Policy Measures? much freight will be expected on the highway system by shifting demand to alternative modes not the focus of tra- As transportation planning and operating agencies strive to ditional highway-oriented planning. It also is listed sepa- improve their efficiency and effectiveness, they have increas- rately because modal diversion of freight is often of inter- ingly turned to performance measures to provide credible, est when reviewing energy and environmental policies, quantitative information to support their analysis and deci- such as the emissions by freight, the energy required to sionmaking. Measurement of transportation system condi- move freight and the greenhouse gases associated with the tion and performance has become an explicitly acknowledged movement of freight. component, not only of the planning process, but also in pro- Policy and economic needs, like the preceding needs, also gramming, budgeting, and system operation. Measures help can be considered as a subset of general and long-range agencies provide accountability to the public, stay focused on planning. These analyses may require more detail by corri- intended results, improve communication with internal and dor or geography. The forecasting process itself may not be external customers, and improve delivery of services. This is

OCR for page 46
48 true not only for general transportation, but also for freight- performance measures. Generally, the performance measure specific policies, programs, and projects. will require detail on the link or system volumes, and the link Table 4.2 shows freight performance measures that might or system average speeds or times. From these forecasts of be used to support the policy needs identified in Step 1. Gen- demand and performance, practitioners can calculate the eral and long-range planning needs and their related per- performance measurements needed to support the analysis of formance measures and required forecasting outputs are not freight policy alternatives. shown in Table 4.2 because they include all of the needs and performance measures that are listed. Although the calcula- Step 3. What Forecasting Models can be tion of performance measures will require additional infor- Used to Support Decisions? mation that will not be available from the forecasting process, such as the administrative, operating, and construction costs As shown in Table 2.1, a framework was developed to organ- associated with a policy, the forecasting outputs are needed to ize the literature review and to examine how different classes of compute the value of these performance measures. Practi- models have been implemented to support public decision- tioners should consult other documents on the use of per- making. As the figure shows, only certain classes of models formance measures. The intent here is to show which fore- have been found useful and applied in support of public deci- casting outputs are required to support the calculation of sionmaking. After identifying the freight outputs that are Table 4.2. Policy needs and corresponding performance measures. Policy Needs Performance Measures Forecasting Outputs Required Average fuel consumption per trip for Modal link volumes, modal link speeds selected trips (or shipments) Fuel consumption per ton-mile traveled Modal link volumes, modal link speed Market share of international or regional Total modal volumes trade by mode Average cost per trip Modal link volumes, modal link speeds Modal Average shipment time, cost, variability in Modal link volumes, modal link speeds Diversion arrival time for freight shipments (local versus long-distance by commodity, by mode) Additional revenue earned by producers Modal link volumes, modal link speeds when shipping via rail Average travel time from facility to Modal link volumes, modal link speeds destination, by mode Administrative, engineering, and Modal link volumes, modal link speeds construction cost per ton-mile (owner cost) Freight transport system supply (route miles, Modal link volumes capacity miles, number of carriers, number of ports/terminals) per "demand unit " (dollar of manufacturing output, ton-mile of Policy and commodity movement, capita, employee, Economic etc.) Miles of freight routes with adequate Modal link volumes, modal link speeds capacity Dollar losses due to freight delays Modal link volumes, modal link speeds Mobility index (ton-miles of travel/vehicle- Modal link volumes, modal link speeds miles of travel times average speed) Project Administrative, engineering, and Modal link volumes, modal link speeds Prioritization construction cost per ton-mile (owner cost) Delay per ton-mile traveled (by mode) Modal link volumes, modal link speeds Exposure (annual average daily traffic and Rail link volumes Rail Planning daily trains) factor for rail crossings Additional revenue earned by producers Modal link volumes, modal link speeds when shipping via rail

OCR for page 46
49 required of the forecasting models, it was decided that it was best be considered as alternate pathways that follow the steps useful to present different categories that should be considered shown in Figure 4.2. to identify which models will best support the calculation of the performance measures. It is useful to consider the selection of Trend analysis--This consists only of Step 9a as shown in the forecasting process from each of the following groupings: Figure 4.2. It directly forecasts freight activity using, at model perspective, model types, and model components. most, historical or economic trends. Commodity forecasting--synthetic modeling of com- modity flows--This consists of Steps 4, 5, and 6, which are Model Perspective used to develop modal commodity flow trip tables, and Although freight is the movement of cargo in vehicles, it Steps, 7, 9, and 10, which are used to convert that commod- makes a considerable difference in developing models to fore- ity trip table to a suitable format for assignment to modal cast freight whether those models are being developed from the networks and then to evaluate the flows on those networks. perspective of the cargo or the perspective of the truck. Fig- Commodity forecasting--direct acquisitions of com- ure 4.1 shows a very simple situation of six stops for the move- modity flows--This consists of Step 6a, which directly ment of four cargo shipments. From the perspective of the acquires a commodity flow table instead of following the cargo, there are four productions (two productions at Stop 0-- synthetic process. If the acquired table includes modal the base, and one each at Stop 1 and Stop 2) and four attrac- flows and these are directly used, this may replace Step 6. tions (one attraction each at Stops 2, 3, 4, and 5). From the per- If not, Step 6 is required. After the modal commodity table spective of the truck as a vehicle, there are six productions and is obtained, Steps 7, 9, and 10 are followed as in the syn- six attractions (one each at Stop 0--the base--and Stops 1 thetic model. through 5). There are five cargo trips as shown by the dotted Economic forecasting--This consists primarily of the feed- lines, while there are six truck trips, as shown by the solid lines. back loop between networks perforce and economic inputs Obviously very different models would be required to forecast shown as Step 6b. Depending on the nature of the economic these movements. This would depend on whether the model model, it may have commodity trip tables that can replace was developed to forecast cargo or trucks. This situation for a some or all of Steps 4, 5, and 6. If the zonal structure in the single truck movement is magnified and compounded when all economic model is different than that used in transporta- of the freight shipments within a study area are considered. 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 Model Types Step 9, this is a required step and will be necessary to deter- Although the model categories in Table 2.1 are useful for mine the correct multiclass highway performance for cataloging model research, an alternate method of classifica- freight trucks. If not included, freight performance in Steps tion is presented based on how the models are applied. It is a 9 and 10 will not consider the interaction with what may variation of the methods in Chapter 6 of NCHRP Report 606: be the majority of trucks on the road. It also is possible that Forecasting Statewide Freight Toolkit. The model types can Step 8 and Steps 4 through 7 are not followed and that 2 3 A Vehicle Trip An O-to-D Commodity Flow 1 4 Example of a five stop pickup and drop truck trip chain involving four origin-to- destination commodity flows 5 Base of Operations Figure 4.1. Illustrative Freight Shipments Cargo and Truck Perspectives.

OCR for page 46
50 Economic Inputs Step 4 Trip Step 6a Generation Acquired Commodity Step 5 Flow Tables Trip Multimodal Distribution Step 6b Step 9a Economic Step 6 By Trend Modeling Mode Choice Mode Analysis Step 7 Step 8 Payload and Service Temporal (nonfreight) Factors trucks Step 9 Modal Assignment Step 10 Benefits Analysis Figure 4.2. Model methods. commodity freight trucks are included with all trucks in in costs by mode regardless of location. For service or non- Step 8. If this is the case, the performance of freight trucks freight trucks, this may mean difference by land use cate- cannot be separated from the performance of all trucks. gories, since truck trips to, from, and between land uses should behave in a similar manner. Validations are classifications that are developed to assist Model Flow Components in model validation. These may not be flows that can be This dimension is intended to capture how the flow vari- expected to behave similarly, but reflect flows that are con- ables are defined in the model steps. The same flow compo- sistent with observable characteristics. Thus, while not all nents need not be in each step. In fact, Step 7 (which is where single-unit trucks or combination tractor-trailer trucks factors are used to convert from annual tons as flow units in might be expected to behave in the same manner, this clas- Steps 4 to 6 to daily truck) is excluded to account for this. sification may be used in the model because it develops vol- However, the flow units can be disaggregated based on the umes that assist in model validation against observed truck need for those units to fulfill specification and calibration rea- classification counts. sons, model validation requirements, or benefits analyses Benefits are classifications developed during benefit calcu- requirements. lations and may reflect classifications that are useful in benefit/impact analysis. While flow in these classifications Behavioral, calibration, and specification classifications are will not necessarily behave similarly nor be consistent with developed during model specification and calibration. They observable validation flows, they may be useful classifi- are intended to develop forecasting methods and equations cations in the benefits/impacts calculation. An example for flows with similar behavior. The freight OD flow tables, would be the use of gross vehicle weight (GVW) for trucks. either produced synthetically or acquired, generally will This may be useful in that different emission rates have have separate tables by commodity. This is because com- been established for different gross vehicle weights, despite modities are expected to behave in similar fashion in trip the fact that vehicles that have the same GVW are not generation to changes in activity drivers, such as employ- expected to behave similarly, and that GVW is not a read- ment; in trip distribution to changes in accessibility such as ily observable characteristic of truck flow on specific high- interzonal composite costs; and in mode choice to changes way links.

OCR for page 46
51 Table 4.3. Tonnage production equations for selected commodities (2002, ktons). SCTG NAICS Description Coefficient T-Stat R2 Cereal Grains (2) 311 Food Manufacturing 0.407 5.11 0.48 Farm Acres (in Thousands) 0.441 4.20 Other Agriculture Products 311 Food Manufacturing 0.188 10.43 0.65 (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 Logs (25) 113 Forestry and Logging 0.323 4.02 0.70 115 Support Activities for Agriculture 0.843 3.91 and Forestry 321 Wood Product Manufacturing 0.465 6.48 Wood Products (26) 321 Wood Product Manufacturing 0.625 18.37 0.75 Newsprint/Paper (27) 113 Forestry and Logging 0.887 13.59 0.73 323 Printing and Related Activities 0.086 7.38 Paper Articles (28) 322 Paper Manufacturing 0.101 10.76 0.81 323 Printing and Related Activities 0.038 4.82 Base Metals (32) 331 Primary Metal Manufacturing 0.424 8.69 0.75 333 Machinery Manufacturing 0.085 3.24 Articles of Base Metals (33) 332 Fabricated Metal Product 0.115 14.51 0.65 Manufacturing Machinery (34) 332 Fabricated Metal Product 0.085 2.92 0.63 Manufacturing 333 Machinery Manufacturing 0.081 2.01 Electronic and Electrical (35) 333 Machinery Manufacturing 0.02 3.00 334 Computer and Electronic Product 0.012 4.35 0.70 Manufacturing 335 Electrical Equipment, Appliance, 0.029 2.44 and Component Manufacturing 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. Step 4. How Much Freight? Trip Generation: ally develop equations for their own study area from a com- Productions and Attractions by modity flow survey for their area. Some general equations have Commodity in Tons been developed for an FHWA project to disaggregate FAF2 data from regions to counties.12 A sample of these equations is This step is necessary for those models that estimate com- shown in Table 4.3. However, any average equations should be modity freight tables synthetically. The volume of commodity used with caution, since the economies of each state and region flows that begin in a zone, called productions, and an end in a are so different that average equations developed for average zone, called attractions, must be determined for each zone. economic conditions can not be expected to apply. Since mode choice is a later step, at this point, the freight flow Additionally, equations for freight productions and attrac- must be expressed in units that are common to all modes. tions can not be expected to apply to all zones. In passenger Commonly, this is tons, although other multimodal units (e.g., forecasting, there are zones that generate significant trips (e.g., value) can be used. To calculate the productions and attrac- airports) not related to employment as an indicator of activity. tions for each zone, the economic drivers of freight must be These zones are treated as special generators where the num- available. These drivers will be some indication of the size (e.g., ber of productions and attractions are directly specified in any as indicated by employment) of the different industries that model forecasts. In freight forecasting, this same treatment as produce or attract (consume) freight. Since shipments of com- special generators is required for ports, rail terminals, and modities can be expected to be associated with different indus- tries, equations relating the freight productions and attractions 12 Cambridge Systematics, Development of a Computerized Method to Subdivide the will be developed for those commodities that are expected to FAF2 Regional Commodity OD Data to County-Level OD Data, FHWA, January respond similarly to certain industries. Public agencies gener- 2009, unpublished report.

OCR for page 46
52 other locations that might be significant producers or attrac- Step 6. What Mode Does Freight Use? Mode tors of freight for commodities, but for where there is no sig- Choice: Trip Table Os and Ds by Mode nificant employment in these zones in the industries associated This step is necessary for those models that estimate com- with those commodities. modity freight tables synthetically. The multimodal tonnages moving between zones must be allocated to the various modes Step 5. Where Does the Freight Go? that are used to transport freight. The choice of mode used by Trip Distribution: Trip Table Os and Ds freight is a complicated process. As discussed in Section 3.5, This step is necessary for those models that estimate com- the choice will be based on many considerations, including modity freight tables synthetically. The distribution of produc- the characteristics of the mode, goods, production zone, and tions from, and attractions to, zones, as calculated in Step 4, attraction zone. When insufficient detail exists to properly must be distributed between all of the zones. Although this dis- model this choice, either because the format and parameters tribution may be based on an existing table of freight flows, of the choice equations or the data on the characteristics are through a Fratar process, the most common means of synthet- not known for the base or forecast year, the future choice of ically distributing trips between zones is through the use of mode is assumed to be the same as the existing choice of a gravity model. In the gravity model for freight, as in other mode. When this model of forecasting mode choice is used, transportation applications, the mathematical equations used as it is in many freight models and in FHWA's FAF, it is are applied separately for flows with similar behavior (e.g., assumed that the factors effecting mode choice are captured commodities). The productions and attractions by commod- in the existing observed mode choice by commodity. Thus, ity are distributed in the gravity model based on the accessibil- when the mode share is forecast to change over time, it ity between the zones, as measured by the impedance between reflects changes in the volume and mix of commodities car- zones. For freight models, the impedance variable for the large ried. For example, in Table 4.4, which is from the FAF2 state geographies considered by freight is most often found to be profile of California, the freight mode share by truck is fore- distance. By examining the commodity flow survey data, it is cast to change from 73 percent in 2002 to 77 percent in 2035. possible to determine those parameters, such as the average However, this is because the forecast of the commodity mix trip length by commodity, which are used to vary the accessi- for California is different from the mix in the base year. A bility in response to changes in the impedance variable. The basic assumption in the FAF2 is that for each commodity in match of the trip length distribution for one commodity in the the FAF2, the mode share in 2035 is the same as it was in 2002. Florida freight model of the observed commodity flow and If the mode share is available for an existing year, that table the estimated flow in a gravity model is shown in Figure 4.3. It of mode shares by commodity can still be examined to find OD is possible that impedance variables other than distance and pairs that perform worse than other OD pairs at the same dis- other distributions may better match observed data. Practi- tance. The mode shares for these markets can be adjusted in tioners are urged to consult freight references such as the a qualitative process to reflect policy changes--for example QRFM to explore this topic in detail. those that might be expected to bring their mode share to aver- 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 Miles Observed Estimated Figure 4.3. Florida freight gravity model results (food products).

OCR for page 46
53 Table 4.4. FAF freight shipments from California by weight (2002 and 2035, millions of tons). 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. age conditions. This accepted forecasting technique for mode vey, as well as the forecast data required to use these models, choice is often referred to as Market Segmentation. is costly, a decision to use that commodity flow directly in the In some cases, it is desirable to develop estimates of mode modeling process may be quite reasonable. choice for markets in which the modal information is limited This step uses an acquired commodity flow survey as a trip (e.g., because the mode was never offered). In this case, adjust- table. Generally, this survey will dictate the behavioral classifi- ing the mode share to match observed averages would not cations used in the model (e.g., the commodities and/or modes apply. For this case and for any case where estimates prepared in the survey will be used in the forecasting model). Additional by a qualitative process are needed, a mode choice model may processing of the acquired table may be necessary to convert be developed. This is not a trivial undertaking and will likely from the geographies used in the survey to the zones used in require a survey to identify the significant variables in the transportation modeling. mode-choice decisions, as well as the coefficients and other parameters that should be applied to these variables. Section Step 6b. Economic/Land Use Model: 3.5 describes a process where the FAF2 commodity flow was Alternate Ways to Get Freight OD used as an RP survey to determine these variables and their Table by Mode parameters. Section 3.5 did find that, consistent with the choice decisions reveled by the FAF2, that modal distance, which pre- The process described produces a trip table where the sumably serves as a surrogate for modal cost and time, is the economic/land use activities that give rise to freight are exoge- most significant explanatory variable in mode choice. 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- Step 6a. Direct Acquisition of Commodity etition of earlier steps, which allows the forecast of economic OD Tables: Alternate Ways to Get activities to be varied in a feedback loop, after the perform- Freight OD Tables ance of the system is calculated in Step 10. The equations required in Steps 4, 5, and 6 to develop a More complex economic models may explicitly include a synthetic freight trip table by mode most likely will be devel- trip generation step (freight produced by zones), a trip distri- oped from a commodity flow survey. Typically, those freight bution step (freight moving between zones), and a mode- surveys, unlike household surveys used in passenger plan- choice step (freight moving between zones), including mode ning, already have been expanded to represent all geographies used. Any or all of these steps may replace the synthetic steps over an extended time period, most often a year. Although described in Steps 4 through 6. The economic model may not these commodity flow surveys may not be formatted like the use the same geographies as the transportation process, there- trip tables used in freight forecasting, it requires little effort to fore, it may be necessary to disaggregate flows of the geogra- reformat these surveys into tables. When a commodity flow phies in the economic model to zones compatible with net- survey has been acquired and developing the trip generation, work assignment. Unless the economic model includes other trip distribution, and mode-choice equations from that sur- nonfreight flows that impact freight performance, there may

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

OCR for page 46
Table 4.6. Comparison of trip rates by truck type and land use. 24 Axles, 6+ Tire, SingleUnit, Truck Type 14,00028,000 Lbs 8,00028,000 Lbs 14,00028,000 Lbs 16,00052,000 Lbs PSRC Truck NWRG Survey NWRG Survey NCHRP 298 NCHRP 298 Model PSRC Truck Model Land Use (Production) (Attraction) (MAG) (SCAG) (Production) (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.

OCR for page 46
56 described in Steps 4 and 5 above. The trip generation rates and to track the assignment of freight trucks in order to report on the trip distribution factors should be developed through the their specific volumes and the paths that they use. use of commercial vehicle surveys. One example of trip rates for nonfreight trucks is shown in Table 4.6. This table, develop for Step 9a. What Facilities Do Freight Vehicles the Southern California Association of Governments (SCAG), Use? Direct Estimation shows rates from other models in order to provide context for the SCAD model development. The development of a non- This step bypasses all of the forecasting steps described freight truck trip table may be an adaptation of an existing total above. It uses the time series models, which consider historical truck table. If this is the case care must be taken to avoid dou- freight flows separately or with other economic factors, to ble counting the trucks that carry freight. It will be necessary to develop freight forecasts. Because those steps are skipped, adjust the total truck trip rates and distributions to account for changes in freight trip generation unrelated to the facility being the freight trucks that are being handled separately. 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 Step 9. What Facilities Do Freight discussed previously. Vehicles Use? Assignment of However, there are instances where a freight model does Modal Vehicles to Networks not include the freight facility for which forecasts are desired. This step assigns the freight trip tables, expressed in modal The decision being considered may not be unique enough to vehicles, to the modal networks. Although public agencies tra- warrant the development of a freight model. Impacts of the ditionally forecast assignment to the highway system, they less freight project on other conditions, for example, on the econ- often forecast assignment to other modal networks. That does omy or the environmental, may be simple or small enough to not mean that Steps 4, 5, and 6 or the alternate processes be ignored. If this is the situation, the forecasts are limited to described above are not worthwhile. Unless freight is addressed a single freight facility, the other impacts are not considered, multimodally, the trip table of freight trucks could consider all and the impacts of the project can be considered simply--a of the multimodal decisions made in moving freight. The dif- trend forecast may be sufficient as a freight forecast. ficulty in making assignments to modal networks is twofold. First the information about the other modal networks may be Step 10. How Do Freight Vehicles Perform limited. The connections, availability, and capacity of the links on the Network? Estimation of Benefits forming the other modal networks may not be readily avail- able, particularly in a format that can be used in assignment. Public decisions are not easily made using the outputs of When the modal networks are available in a format that can be transportation forecasting models. Public decisions are based used in assignment models (e.g., all nodal connectivity issues on how the scenarios examined produce benefits for the users, have been addressed and zonal connectors have been added), business, and society. The benefits, cost, and impacts of trans- the whole issue about how these routing decisions are made portation projects need to be evaluated not only against other must be decided. The routing decisions of freight over the rail- transportation projects, but against other public investments road, air, and water networks reflects business decisions that and policy decisions. To make these comparisons, it is cus- are in no way similar to the multiclass user equilibrium assign- tomary to process the outputs of transportation models--the ment routines used by highways. For the TnDOT and the volumes and performance of vehicles on modal networks-- Association of American Railroads (AAR), rail assignments into other more generic impacts such as direct and indirect have been prepared that assign rail trip tables using shortest costs, emissions of green house gases and other pollutants, and distance assignments that do not consider operational or economic development. capacity diversions. This step considers the use of models to calculate the bene- When truck freight assignments are made to the highway fits and impacts of freight transportation projects, policies, and system, it must be recognized that freight trucks are not the programs. Most benefits evaluations recognize that transporta- only vehicles, much less the only trucks, using the road. The tion activities, including freight, can impact the system in mul- performance of freight trucks on the highway network should tiple ways. For example, Moving Cooler,13 calculates emission consider the assignment of the freight truck table, together with impacts from transportation as being related to changes in the nonfreight trucks discussed in Step 8, as well as all other demand, operation, vehicle technology, and fuel. The freight vehicles, such as autos that use the highway. These multiclass forecasting steps described above can not consider vehicle user equilibrium highway assignments already are customarily being prepared by transportation practitioners in support of 13Cambridge Systematics, Inc., Moving Cooler: An Analysis of Transportation public decisionmaking. For freight planning, it is also necessary Strategies for Reducing Greenhouse Gas Emissions, Urban Land Institute, July 2009.