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This page is part of the Category Statewide Models.

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Corridor-level Models

Freight Models

Travel demand modeling has traditionally focused on person travel by auto. This is not surprising, as autos generate more than 90% of all vehicle-miles traveled (FHWA 2016b). However, trucks generate the core demand for transportation infrastructure maintenance. Trucks also consume 25% of all fuel in the United States (BTS 2016), contributing disproportionately to greenhouse gas (GHG) emissions. Furthermore, growth in freight transportation is expected to significantly outpace growth in passenger transportation (Chow et al. 2010, p. 1012). Given their disproportional impact upon the transportation system, it is not surprising that most statewide models account for freight modeling, particularly in areas with high levels of congestion.

Current Models and Approaches

As freight tends to make up a higher share of traffic on rural roads, statewide models tend to have a larger share of freight traffic than urban models. Therefore, statewide models tend to pay more attention to freight flows, often distinguishing short- and long-distance freight flows.

Short-distance Truck Modeling

While short-distance trucks are covered by 21 states (62% of all states with statewide models), long-distance trucks are modeled by 26 states (76%).Connecticut uses static truck trips tables, and Nebraska plans to add them within the next year. Doing so enables these states to at least account for truck volumes on the network, even though truck flows would not be scenario sensitive.

Of the 21 states that model short-distance trucks, 19 use trip-based models, and only Ohio and Oregon use tour-based truck models. The limitations of trip-based truck models have been discussed extensively in the literature, yet it is no surprise that tour-based models are uncommon in statewide models. The heterogeneous travel behavior of trucks (depending, among other factors, on truck type and commodities carried) and the limited freight data availability (much more so than for auto travel) make it inherently challenging to represent tour-based travel behavior for trucks. However, a few operational tour-based models in addition to Ohio and Oregon have been implemented for Alberta (Hunt and Stefan 2007), Guatemala City (Holguín-Veras and Thorson 2003), Rome (Nuzzolo and Comi 2013), and the San Pedro Bay Ports in Southern California (You 2012). Given the increasing interest in freight in many states, it is expected that more will follow the examples of Ohio and Oregon in tour-based truck modeling in the future.

Long-distance Truck Modeling

Long-distance truck modeling is dominated by commodity flow models (Figure 24). Illinois uses a supply chain model, though publicly available data for such modeling approach is very limited. Most of the respondents who reported using commodity flow models in the survey reported that they are based, at least in part, upon origin-destination freight flow data from the Freight Analysis Framework (FAF), as described in chapter four, “Traditional Freight Travel Data.” Presumably, many of these models are not policy-sensitive commodity flow models, but rather static transformations of exogenous FAF commodity flows converted into truck flows. Nine states use FAF payload factors to convert freight flows in tons into truckload equivalents.

Mode Choice

A growing number of states apply mode choice models to freight flows as well. Of 26 states that model long-distance freight flows, six states (23%) apply rule-based freight mode choice models. Such models do not attempt to econometrically estimate mode shares, but rather apply simple rules of modal allocation that can be reviewed and changed. For example, rules may include that short-distance flows rarely use rail or water modes, only high-value goods move by air, and vessels can only be used if there is a waterway on at least part of the trip. Logit-based freight mode choice models were implemented by Florida, Georgia, Illinois, Ohio, Oregon, Texas, and Virginia. Although such models provide rich information on driving factors for mode choice, data limitations often make it challenging to reasonably estimate these models. Many of these logit-based models are designed as so-called freight diversion models (i.e., they model the shift from one mode, such as truck, to another mode, such as rail). Starting with the observed mode share and modeling only the potential shift from one mode to another is a powerful way to deal with data limitations in freight modeling while maintaining some freight mode sensitivities to policy scenarios. Ohio and Oregon use a combination of both rule-based and logit-based mode choice models. About half of the 26 states that model freight long-distance flows do not model freight mode choice at all, but instead generate truck flows only. Of the 11 statewide models that represent freight mode choice, all include truck and rail as modal options (Figure 26). Water and air are modeled in eight and seven states, respectively. California, Ohio, Oregon, and Utah even model pipelines, a flow that is inherently difficult to represent because it has the least amount of data available.

High Speed Rail Models

California High-Speed Rail Model

There are a few high-speed rail models developed and implemented in the United States. One such model is the California High-Speed Rail Ridership and Revenue Model. The California High­Speed Rail Authority maintains a separate ridership forecasting model used to support business and system planning, as well as corridor studies and analysis of alternative alignments and phasing of implementation. It is a complete statewide model, and uses some of the same data used to develop the Caltrans California Statewide Travel Demand Model. It incorporates a more sophisticated mode choice model better suited for evaluating HSR alternatives, but otherwise covers the same travel markets as the Caltrans model, and at comparable levels of spatial, temporal, and behavioral resolution. It also includes an explicit risk and uncertainty assessment process. This is a necessity for HSR forecasting, but unfortunately unique among the statewide models.

Bi-level Modeling Framework

The modeling system employs the bi­-level structure. The long­-distance model includes trips within the state longer than 50 miles, stratified by four trip purposes. They include business, commuting, recreational, and all other trips, a common scheme used in long-­distance travel models. The trip frequency and destination choice models were estimated using the a statewide household long-distance survey data and a RP/SP survey. The mode choice model is a combined model of main, access, and egress mode choice.

The short-­distance model includes person trip tables by mode and trip purpose from the regional travel models used by the Southern California Association of Governments (SCAG) and the Metropolitan Transportation Commission (MTC). They are the MPOs for the Los Angeles Basin and San Francisco Bay Areas, respectively. Trip tables for their base and forecast years are used to represent short-­distance travel . The mode choice model is an adaptation of the 1996 Baycast model developed for MTC, which has been calibrated to reproduce base year transit ridership within each metropolitan area. The resulting system provides consistent forecasts of short­-distance mode choice within both regions. The station spacing outside of those regions is too far apart to enable short-­distance HSR trips, obviating the need for short­-distance models within the rest of the state.


The current model and its predecessors have been used to generate ridership and revenue estimates for initial and detailed system planning and in support of corridor studies and evaluation of candidate initial operating segments. This has included the environmental studies required at all levels of analyses for the program, and use for station-­level impact analyses. The latter has required post-­processing of the model outputs, for the model was not designed to support detailed analyses at a fine level of geography.

Multi-state Models

Some statewide models incorporate parts of adjacent states, some of which had almost as much detail as the statewide model in that state. Urban areas beyond the state border, especially when they are agglomerations, heavily influence both person and freight traffic patterns. The ability to bring the effect of important nearby markets into the model was one of the driving motivations for building the Chesapeake megaregional model. The benefits obtained from doing so were clear. There has been surprisingly little interest in consolidating resources by building multi-state or megaregional models, despite the apparent benefits. Several reasons were cited for this:

  • Lack of control over model design, development priorities, or delivery deadlines;
  • Increased effort required to run the model, owing to increased coordination with and data supplied by other states involved;
  • Unique analytical requirements that other states do not have; and
  • A desire to retain capability to quickly adapt or change the model if required to meet new analytical requirements.

These requirements appear to outweigh the potential for cost- and data-sharing, and ability to satisfy common goals for model functionality and elimination of boundary effects at state borders. Moreover, computational and institutional issues will need to be overcome before multi-state models emerge in practice.

National models

National models strongly relate to statewide models since they use similar data sources and the movement to develop national models came from a statewide modeling committee research suggestion. The conventional thinking is that an accurate national model could be used as a source of information – networks, trip table, standard attributes – for developing more detailed statewide models. The National Travel Demand Forecasting Model Phase I Final Scope report developed a framework for developing a national model. The scope had the following components:

  • Identify alternative model structures;
  • Obtain and prepare input data;
  • Model development and validation;
  • Develop tools and documentation; and
  • Future directions.

There has been significant movement in starting the development of the national model by FHWA, Office of Policy. This agency has started research on new sources of data for long distance travel as a first step in developing the national model.

Provincial models