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Statewide Travel Forecasting Models (2006)

Chapter: Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting

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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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Suggested Citation:"Appendix C - Literature on Statewide and Intercity Passenger Travel Forecasting." National Academies of Sciences, Engineering, and Medicine. 2006. Statewide Travel Forecasting Models. Washington, DC: The National Academies Press. doi: 10.17226/13958.
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This appendix was principally written by David Farmer, with contributions from Alan Horowitz. The material has been ex- cerpted from the Guidebook on Statewide Travel Forecasting Models. INTERCITY PASSENGER LITERATURE Intercity travel is a broad heading that includes statewide travel. As used here, the term “intercity” forecasting involves the prediction and assignment of traffic volumes between cities or other points of interest that are separated by some significant distance. The term intercity is also used to distin- guish these models from “urban” models, which typically in- volve travel between more closely spaced points of interest within a localized area. Intercity models include corridor, statewide, regional, and national models. Statewide models are therefore a subset of intercity models. The main point, first expressed as early as 1960 (C1,C2), is that the charac- teristics of intercity travel are inherently different from those of travel within an urban area. It is assumed that people travel according to a somewhat different set of rules over longer distances and between metropolitan areas. The intercity models encountered in the literature are often associated with an academic exercise, and therefore make use of fewer, more carefully chosen origin–destination (OD) pairs than would normally be included in a meaningful statewide model. Con- sequently, they generally present situations that are a little more abstract in nature. The similarities to statewide models are many. Types of Intercity Passenger Models A number of reviews have been made of the early history of intercity modeling (C3–C7) and most include some discus- sion of the taxonomy of intercity models. Intercity models can essentially be divided into four types on the basis of two cat- egories: data and structure. The models can make use of either aggregate or disaggregate data, and can be of a direct-demand or sequential structure. The four resulting combinations are: (1) aggregate direct-demand models, (2) aggregate sequential models, (3) disaggregate direct-demand models, and (4) dis- aggregate sequential models. Intercity travel demand models can be further classified by whether they encompass only a single mode (mode-specific) or multiple modes (total de- mand), and by which trip purposes they include. Aggregate data make use of the socioeconomic data for the OD pairs in the model and can also include the service charac- teristics of the modes of travel between them. Disaggregate data go further to examine the motives and characteristics of the trip makers at an individual or household level and are typically used to generate the probability that a particular trip is taken or mode is used. In terms of model structure, a direct-demand model is one that calculates all of the desired travel information in one, singly calibrated step. (Direct-demand models are sometimes called econometric models because of their resem- blance to statistical models of economic demand.) A sequential model, on the other hand, divides the modeling process into several individually calibrated steps. The urban “four-step” modeling process, which many departments of transportation (DOTs) have adopted for the statewide modeling purposes, pre- sents the quintessential example of a sequential model. Aggregate Direct-Demand Models The earliest intercity models were of the direct-demand type and were developed in the 1960s as part of an examination of the Northeast Corridor (C6). The most famous of these was Quandt and Baumol’s abstract mode model (C8). The reader is referred to the reviews referenced in the previous section [especially Koppelman et al. (C6)] for a more com- plete historical perspective of significant intercity modeling efforts. The following direct-demand models—some of which are not mentioned in those references—are noted here because they possess features that might prove useful to modeling at the statewide level. A notable early innovation was attempted by Yu (C9). Yu took the standard direct-demand formulation—regressed from cross-sectional data—and recognized that the elastici- ties present in the cross-sectional data would not necessarily remain constant over time. His paper presents two single- purpose (one for business travel and one for personal travel) direct-demand models in which the regression coefficients each include a time–series component. It is a novel idea that does not appear to have been picked up by succeeding au- thors. Another innovative idea is found in Cohen et al. (C10). Here, as part of two single-purpose (business and nonbusi- ness) direct-demand models, the authors propose to use a pivot-point procedure. The procedure is intended to elimi- nate the effects (on the traffic volumes to be forecasted) that result from variables that have been excluded from the mod- els. Description of the pivot-point procedure is brief, how- ever, and use of this procedure does not seem to have been adopted by other researchers. By the late 1970s, direct-demand models were being con- structed to include an increasingly wider range of variables to account for the enormous variety of factors that influence travel behavior. Models presented by Peers and Bevilacqua APPENDIX C Literature on Statewide and Intercity Passenger Travel Forecasting 79

80 (C11) and Kaplan et al. (C12) give some sense of this trend. Peers and Bevilacqua describe a model that includes a long list of policy-sensitive variables, arranged into three groups: (1) extensive variables, including population and employ- ment; (2) intensive variables, including persons per house- hold, income per household, and employment per acre; and (3) system variables, including travel speeds and costs. Meanwhile, Kaplan et al. describe their Passenger Oriented Intercity Network Travel Simulation (POINTS) model, a multimodal model that explicitly includes consideration of accessibility to the transportation system. Both of these mod- els provide a bridge from an earlier emphasis on aggregate modeling to the growth in disaggregate modeling research by the early 1980s. Disaggregate Sequential Models One of the first applications of disaggregate (or behavioral) modeling was for the mode-choice step of sequential models. It is possible to develop a mode-choice model without disag- gregate data, as DiRenzo and Rossi did, using a “reasoned diversion model” (C13). Disaggregate models, however, typ- ically use a logit formulation to provide a convenient way of including a number of mode-abstract, transportation accessi- bility, policy-related, and behaviorally based variables in the modeling process. Owing to parallel research in urban area forecasting in the early 1980s, these models became more at- tractive. They were thought to be especially useful in the ef- fort to estimate the shifts in mode share that were expected from deregulation in the air and intercity bus industries, and from the anticipated implementation of high-speed rail trans- portation (C14,C15). Again, Koppelman et al. (C6) provides a review of many of the earlier disaggregate mode choice models. In addition, Miller (C16), Forinash (C17), and Forinash and Koppelman (C18) provide studies of the various structures (binomial, multinomial, and nested-multinomial) available to more realistically represent the cross-elasticities between modes and to eliminate irrelevant alternatives in the logit mode-split formulation. Armed with an increasing understanding about the imple- mentation of disaggregate modeling techniques and fueled by the increasing availability of disaggregate data, several re- searchers have developed complete travel-demand models based on the analysis of disaggregate data in a number of dis- crete, nested steps. Morrison and Winston, for example, pres- ent multimodal models (one for vacation travel and one for business) with the hierarchical structure shown in Figure C1 (C19). Similarly, Koppelman (C20) and Koppelman and Hirsh (C21,C22) present a multimodal model with a structure shown in Figure C2. Morrison and Winston make use of the 1977 National Travel Survey data, whereas Koppelman and Hirsh use both the National Travel Survey and the 1977 National Personal Transportation Survey (NPTS) data. Both pairs of researchers sought to use this disaggregate data in a model structure that mimics the behavioral logic of trip making. One Disaggregate Direct-Demand Model Another model of interest is the disaggregate direct-demand model developed in the 1980s by the Egypt National Trans- portation Study (C23–C25). The Egyptian Intercity Trans- portation Planning Model estimates travel on seven modes for travelers in three income levels. It is unusual in its use of disaggregate data in a single equation (direct-demand) for- mat. Also, unlike many intercity passenger models, it in- cludes capacity restraints on the network, most notably for the shortage of passenger rail cars. Because it deals with a very practical situation, the Egyptian model could reasonably be noted in the section of this appendix describing statewide forecasting techniques; however, because the transportation situation in Egypt is sufficiently an abstraction of the situa- tion in the United States, it seems fitting to include it with the intercity models. It might also be noted that, in its treatment of rail car capacity restraints, it resembles some freight mod- els, as well. Make Trip Destination 1 Mode 1 Rent a car Don't rent a car Mode 2 Mode j Destination 2 Destination i FIGURE C1 Structure of Morrison and Winston’s model. Trip Frequency One Destination 1 Mode 1 Service Class 1 Service Class 2 Service Class k Mode 2 Mode j Destination 2 Destination i None FIGURE C2 Structure of Koppelman and Hirsh’s model.

81 Single-Mode and Single-Purpose Models Besides the ubiquitous single-mode automobile models, there are two other types of single-mode models of interest: bus and air. (Most passenger rail models are a part of a mul- timodal model.) Modeling of intercity bus travel has proven to be difficult (C26) and examples of intercity bus models are rare. One interesting bus model is presented by Neumann and Byrne (C27). His model describes a probabilistic (disaggre- gate) model based on a Poisson distribution of ridership, as opposed to a regression model. He concludes that this for- mulation provides a simpler and more reasonable estimate of ridership on rural bus routes. Several air travel models are also of interest. As early as the 1960s it was recognized that the year-to-year growth in air travel makes the use of time–series techniques valuable, and a 1968 paper by Brown and Watkins (C28) addresses this issue with simple linear regression techniques. A later paper by Oberhausen and Koppelman (C29) also looks at time– series analysis of air traffic patterns using a Box–Jenkins procedure to account for cyclical (seasonal and yearly) vari- ations in travel behavior. In another study, Pickrell (C30) uses a combination of techniques to assess future trends in intercity air travel. Pickrell uses a single-mode direct- demand model to estimate the total demand for air travel. At the same time, he uses an aggregate mode-choice model to predict the percentage of market share that the air mode could generate under several alternative futures. Other air travel models of interest include a regression analysis of travel between small cities in Iowa by Thorson and Brewer (C31), and an elaborate direct-demand model of intercity air travel based on quality-of-service measures by Ghobrial and Kanafani (C32). Finally, the one other single-purpose intercity model worth noting is the disaggregate model of recreational travel presented by Gilbert (C33). Gilbert’s model is suffi- ciently abstract to be included here with the other intercity models, but more will be said about recreational travel models in Section 2. It should be sufficient to state here that Gilbert’s paper, published in 1974, is one of the latest papers found to specifically address the recreational trip purpose. Discussion As will be seen in the following sections, the intercity fore- casting techniques employed in most existing statewide models are principally those of the aggregate sequential type. This is partly owing to the strong traditions of and training in the four-step modeling process, but it is also the result of the general failure of disaggregate techniques at a statewide scale. Although disaggregate models are attractive because of their ability to include the behavioral aspects of travel, their principal drawback is the lack of sufficient disaggregate data for calibration of statistically meaningful statewide models. Until further data are available, their use will remain limited. It should also be noted that there is a place for aggregate direct-demand models at a statewide scale. This econometric type of model can be especially useful in tying the forecast of single quantity (e.g., annual vehicle-miles traveled or emissions) to forecasts of socioeconomic data. STATEWIDE PASSENGER FORECASTING LITERATURE Despite the amount of research involving the characteristics of intercity travel and its concentrations on econometric models and probability-based models, passenger travel fore- casting, as practiced by the various state DOTs, has remained much more basic. In most of the states contacted as part of the research for this appendix no travel modeling is done on a statewide level. At the majority of state DOTs, forecasting is done for specific projects only, and forecasts are made based on historic trends, rather than on some formal model. For the states that are engaged in some type of modeling process, the models used are all “four-step” models, with a modeling procedure borrowed almost entirely from the ur- ban transportation planning (UTP) process. This is likely a function of the ready availability of urban modeling soft- ware and personnel trained to use it. As early as 1967, Ari- zona and Illinois had developed UTP-style models (C34), and by 1972 at least 19 different states were using or prepar- ing statewide models (C35). Modeling activities were evi- dently so popular that in 1973 FHWA perceived the need to standardize the thinking about statewide modeling, and issued a guidebook on the subject (C36)—effectively insti- tutionalizing the UTP-style model for statewide use. The enthusiasm for developing statewide models that was pres- ent in the late 1960s and early 1970s soon waned, however, whether owing to funding cuts or to frustration with the model results, and little activity seems to have taken place [studies in Florida and Kansas (C37–C39) were an excep- tion until very recently]. Apparently, only Connecticut, Kentucky, and Michigan have been continuously develop- ing models from the earlier period. By the early 1990s, prompted by new federal legislation (Clean Air Act Amendment of 1990 and Intermodal Surface Transportation Efficiency Act of 1991), several states were rethinking their strategies. New Mexico (C40) and Texas (C41) produced interesting reports that outline this renewed focus on statewide modeling. The New Mexico report ad- dresses both passenger and goods movement models within the broader context of statewide transportation planning. The Texas report, which includes reviews of circa-1990 models from Florida, Kentucky, and Michigan, concentrates more on the details of statewide modeling, especially the difficulties in isolating interzonal trips and the proliferation of “K-factors” in recent models. Despite this promising trend,

82 neither New Mexico nor Texas is currently involved in statewide modeling. (Texas is, however, scheduled to issue a request for proposal for a model development contract in the fall of 1997.] A list of states contacted that sent information about their current passenger modeling efforts is presented in Table C1, and these are discussed below. Data Collection for Passenger Travel Ideally, travel forecasts are based on some sort of travel data. One obvious source of travel data is the survey. Surveys have been conducted at the statewide level since the earliest days of highway modeling (C42), and continue to be conducted at State TAZs Modes Purposes Comments Connecticut 1,300 total 1. SOV 2. HOV 3. Bus 4. Rail 1. HBW 2. HBNW 3. NHB • • • • • • • • • • • • • • • • • • • • • • • • Mode split based on LOS information Iterative-equilibrium assignment for highways Florida 440 internal 32 external Highway vehicles only 1. HBW 2. HB shop 3. HB soc./rec. 4. HB misc. 5. NHB 6. Truck/taxi All trips are modeled to maximize use of MPO models Gravity friction factors based on MPO urban models Mode split is auto occupancy only based on production zone Extensive use of K-factors Indiana 500 internal 50–60 external 1. Auto 2. Truck 3. Transit 1. HBW 2. Other business 3. HBO 4. NHB 5. Recreational 6. Truck Under development Internal TAZs at the township level Aggregate mode choice Kentucky 756 internal 706 external Auto only 1. HBW 2. HBO 3. NHB Model includes a large portion of surrounding states NPTS national average data used for trip generation Michigan 2,392 total Auto only 1. HB work/biz. 2. HB soc./rec./vac. 3. HBO 4. NHB work/biz. 5. NHB other All trips modeled—previous models did not consider local trips Two possible mode split models: (1) simple cross-classification and (2) LOS-based LOS-based mode split model still under development NTPS data used for calibration; CTPP data used for validation Extensive use of K-factors New Hampshire 1 per 5,000 pop. 1. SOV 2. HOV2 3. HOV3+ 4. Bus 5. Rail 1. HBW 2. Business related 3. Personal 4. Shopping 5. Recreational 6. Other Under development Logit trip generation and distribution Time of day and seasonal factors New Jersey 2,762 internal 51 external — — Model created by merging five MPO models Vermont 622 internal 70 external Highway vehicles only 1. HBW 2. HB shop 3. HB school 4. HBO 5. NHB 7.Truck Based on extensive statewide survey Wisconsin 112 internal 45 external 1. Auto 2. Air 3. Rail 4. Bus 1. Business 2. Other Under development No external trips considered Network used only to develop impe- dances for mode share calculations Wyoming 5 internal 5 external 1. Auto 2. Truck — Model created mostly to demonstrate techniques Summer weekend travel is modeled Full trip tables estimated using entropy maximization technique Notes: TAZ = transportation analysis zone; SOV = single-occupancy vehicle; HOV = high-occupancy vehicle; HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome-based; MPO = metropolitan planning organization; LOS = level of service; CTPP = Census Transportation Planning Package; HBO = home-based other; NPTS = National Personal Transportation Survey. • • • TABLE C1 CURRENT STATEWIDE PASSENGER MODELS

83 the statewide level (C43,C44). However, they are relatively expensive to conduct and must be supplemented by other data. Two other options make use of data that are already available: federal survey data and statewide traffic counts. U.S. Census data have always been valuable as inputs to travel modeling. The 1990 Census improved on this by in- cluding a journey-to-work survey, and by introducing the Census Transportation Planning Package (C45). The journey-to-work has proven especially useful in estimating home-based work trips on a statewide level, but has been criticized for its lack of information about other purposes (C46). The Census Transportation Planning Package pro- vides transportation-related information at a transportation analysis zone level, which can be readily aggregated into township- or county-level data for statewide modeling. Another federal data source is provided by the U.S.DOT, which conducted its most recent NPTS in 1995. The NPTS data, which measure some intercity travel, have been used in the development of a number of statewide models. In addi- tion to the aforementioned federal government sources, it should also be noted that estimated and forecasted data are also available from a wide variety of state, academic, and commercial sources. Of course, for many years state DOTs have had in place systems of traffic counting equipment operating at a statewide scale. Research in the early 1980s (C47–C49) developed statistical methods of clustering together traffic counts on different roads based on their similar functional and geographical characteristics. In association with the in- troduction of FHWA’s Traffic Monitoring Guide in 1985 (C50), Pennsylvania (C51), Washington State (C52–C54), and New Mexico (C55,C56) began to reevaluate their traffic monitoring systems to take advantage of clustering. The re- sult is a larger and more statistically valid collection of traf- fic count data available for use in travel forecasting. Data Synthesis for Passenger Travel Even with advanced systems for traffic data collection, it is difficult for a state DOT to collect enough data to account for all of the likely paths between OD pairs being examined. To get around this difficulty, optimization methods have been de- veloped to synthesize trip tables from available traffic count information (C57–C59). These methods have subsequently been applied to statewide analyses in Wyoming (C60,C61). Attempts have also been made to synthesize trip tables from census data at a sub-state level in New Jersey (C62). Trend Analyses of Passenger Travel As noted earlier, many of the DOT officials contacted for this appendix indicated that the only forecasts they make are not based on models, but are instead based on the extrapolation of trends observed in historical data. The Minnesota DOT has formalized this process as it applies to forecasting traffic for their state trunk highways (C63); however, such docu- mentation seems to be the exception. Some indication of the possibilities of trendline analysis is given in a paper by Harmatuck (C64) for the Wisconsin DOT. In it he provides further insight into the particular ways of dealing with traffic data as a time–series. In addition, at least one state contacted for this appendix indicated that a growth factor method, sim- ilar to the method outlined for updating coverage counts in FHWA’s 1992 Traffic Monitoring Guide (C65), is used for forecasting purposes. Otherwise little information is avail- able on travel forecasting techniques in the absence of a statewide model. Statewide Models of Passenger Travel Of the states contacted as part of the research for this appen- dix, those having ongoing modeling efforts sent documenta- tion of their progress. A summary of the passenger models in existence or under development is presented in Table C1. This includes work done in Connecticut (C66,C67), Florida (C68,C69), Indiana (C70), Kentucky (C71), Michigan (C72), New Hampshire (C73), New Jersey (C74,C75), Vermont (C76), Wisconsin (C77), and Wyoming (C60,C61). In addi- tion to the states cited in Table C1, California has a statewide model, but it is being redesigned; therefore, documentation is currently unavailable. Oregon is also in the early stages of developing a comprehensive forecasting model that will in- clude a land use element (C78). Several other states are currently in the initial stages of modeling projects—issuing requests for proposals to interested consultants. As can be seen from Table C1, most of the models con- sider a large number of trip types (as many as five or six), but only a few modes. All of the models are of the four-step style. All use fairly standard UTP procedures, except for the model under development for New Hampshire. New Hampshire proposes to use logit formulations for trip generation and distribution. The Wisconsin model is unique in that it is es- sentially an intercounty model, with comparatively few transportation analysis zones. The Florida and New Jersey models are also interesting in the degree to which they have attempted to incorporate existing metropolitan planning or- ganization models into the statewide modeling effort. The Kentucky and Michigan models are two of the more recent useable models from states with long histories of model development and are representative of the current state of the practice. Recreational Travel Models As early as 1963, recreational trips were considered an im- portant enough purpose to warrant separate study (C79). Indeed, in the late 1960s and early 1970s NCHRP (C80), Indiana (C81,C82), Kentucky (C83,C84), and other states (C85,C86) conducted studies of the special characteristics of recreational travel. However, although Americans seem to

84 have dedicated an increasing amount of time to pursuing recreational activities, the last of these studies was published more than 20 years ago. Because many state economies de- pend heavily on recreational activities, it would seem that this trip type might be important enough to require a closer examination than it has received in the past two decades. Discussion Using trendline procedures in statewide forecasting is prob- ably better than not forecasting at all, especially for short- term planning horizons where large variations from recent trends are less likely. The use of travel forecasting models, however, grounds the forecast in the underlying statewide and national socioeconomic trends. Although these socio- economic trends are themselves forecasts, it is hoped that they broaden the basis of the transportation model suffi- ciently to provide a more reasonable forecast of future travel. REFERENCES C1. Church, D.E., “Outlook for Better Regional and Na- tional Forecasts of Highway Traffic and Finance,” HRB Bulletin 257, Highway Research Board, National Research Council, Washington, D.C., 1960, pp. 36–38. C2. Burch, J.S., “Traffic Interactance Between Cities,” HRB Bulletin 297, Highway Research Board, National Research Council, Washington, D.C., 1961, pp. 14–17. C3. Vogt, Ivers and Associates, NCHRP Report 70: Social and Economic Factors Affecting Intercity Travel, Highway Research Board, National Research Council, Washington, D.C., 1969. C4. Watson, P.L., “Comparison of the Model Structure and Predictive Power of Aggregate and Disaggregate Mod- els of Intercity Mode Choice,” Transportation Research Record 527, Transportation Research Board, National Research Council, Washington, D.C., 1974, pp. 59–65. C5. Rallis, T., Intercity Transport: Engineering and Plan- ning, Wiley, New York, N.Y., 1977. C6. Koppelman, F.S., G.-K. Kuah, and M. 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TRB’s National Cooperative Highway Research Program (NCHRP) Synthesis 358: Statewide Travel Forecasting Models examines statewide travel forecasting models designed to address planning needs and provide forecasts for statewide transportation, including passenger vehicle and freight movements. The report explores the types and purposes of models being used, integration of state and urban models, data requirements, computer needs, resources (including time, funding, training, and staff), limitations, and overall benefits. The report includes five case studies, two that focus on passenger components, two on freight components, and one on both passenger and freight.

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