This chapter illustrates and assesses some of the possible applications of the mode choice models estimated as a part of this project. To give the numerical experiments a real-life flavor, the team decided to use some of the cases studies conducted as part of the project as inspiration for the numerical experiments. Close examination of the case studies selected by the panel, discussed in Chapter 9, revealed that the two projects that are most suitable to illustrate the applications of the mode choice models are the Heartland Corridor and Crescent Corridor. These two cases were picked because they have sufficient basic information to build meaningful scenarios in order to exemplify the possible applications of the models. It is worth noting that the applications discussed in this chapter are only intended to illustrate the use of the models, as they do not purport to be an evaluation of the real-life impacts of the selected projects. Conducting real-life case studies requires access to basic data about demand patterns, as well as detailed data about network conditions before and after the implementation of network changes. Assembling such data requires a level of effort that is not within the scope of this project. The numerical experiments conducted in this section are nothing more than “real-life inspired scenarios.”
To construct these scenarios, basic details of the projects, publicly available data, and reasonable assumptions are used. The analyses demonstrate the application of both types of mode choice models: a) market-share mode choice models, and b) shipment-level mode choice models. The various applications of the models are shown in Table 57. The application of the market-share models to the scenario inspired by the Heartland Corridor estimated the impact of a shorter route on mode split for selected commodity types. The analysis for Crescent Corridor focused on the impact of travel distance and transit times on mode split for selected commodity types. The application of shipment-level models for the Heartland and Crescent Corridors focused on estimating the mode split along each corridor, for selected commodity groups. The commodity types used in these analyses are based on the Standard Classification of Transported Goods (SCTG) codes. In conducting the experiment, the effective hours of service for truck drivers based on current regulation was accounted for within total transit time for shipments (Federal Motor Carrier Safety Administration 2011). An average truck size of 25 tons was employed; therefore, any shipment exceeding this threshold is assigned to another truck(s).
Two different mechanisms were used to estimate shipment sizes and the associated freight rates. In the first case, the team used the shipment size models estimated by the team (see Chapter 8) to estimate the freight rates. In the second case, the team used the Public Use Microdata Sample (PUMS) dataset, to obtain the average shipment sizes by commodity for the hypothetical flows. In terms of which set of results is the most reliable, it all depends on the quality of the estimates for the shipment sizes. The shipment size models are based on the entire Commodity Flow Survey (CFS) microdata and, as a result, they represent the national average pattern. However, since the shipment size patterns may be dissimilar for different freight corridors, the national average pattern may not necessarily capture the shipment patterns at specific corridors. If the shipment size data for a given corridor are small or of questionable quality, the shipment size models may provide the best option. Conversely, if the corridor specific shipment size data are large and of good quality it would make sense to use them instead of the shipment size models.
The following sections succinctly describe the real-life corridors that inspired the numerical scenarios used to test the freight mode choice models, discuss the scenarios, and the corresponding numerical results. The Guidebook produced as a part of this project will detail the methodology of these numerical experiments.