Gregory D. Erhardt

Ohio Long-Distance Travel Model

Credible forecasts of long-distance travel are an important tool for evaluating proposed intercity transportation improvements, including intercity highway and transit projects. Although researchers have studied the topic and have developed frameworks for modeling long-distance travel behavior, these research models have not been integrated into comprehensive model systems used for a wide range of applications. This paper presents a long-distance travel model that bridges the gap between research and practice. It is based on a rigorous behavioral framework that models the unique aspects of long-distance travel, such as a less regular frequency of trips and a different set of modal alternatives. The model structure includes the choice of whether to travel, the selection of the days on which to travel, scheduling to a specific time of day, destination choice, and mode choice. The model is sensitive to important descriptive variables, including the demographic characteristics of travelers, the attractiveness of possible destinations, and the levels of service of air, transit, and highway networks. It has been successfully implemented as part of the Ohio statewide model, which also features an advanced tour-based model of short-distance travel. Through this integration, it allows for behavioral consistency within the entire model system and competition among all travelers for transportation capacity. Lessons are learned about the data needs and research needs to further improve long-distance travel models.

MODELING THE CHOICE TO USE TOLL AND HIGH-OCCUPANCY VEHICLE FACILITIES

Traditionally, mode choice models distinguish between drive-alone and shared ride modes, leaving the network assignment models to predict the assignment of vehicles to toll and high-occupancy vehicle (HOV) facilities. If the shortest generalized cost path in the user equilibrium assignment is a toll or HOV path, the trip becomes a toll or HOV trip. Mode choice models that include the use of general-purpose highways, toll roads, and HOV lanes simultaneously with the choice of the drive-alone and shared ride modes are developed. Multinomial logit and nested logit models are estimated for this full set of alternatives. The models are estimated from a sample of data enriched by special surveys of toll, HOV, and transit users in the Houston, Texas, region. These data provide an empirical basis for studying the behaviors of toll and HOV facility users that is not normally available. The results indicate that the time saved by using these facilities has a higher utility weight than the time differences between other modes. Furthermore, for each mile traveled on a toll or HOV facility, there is an additional benefit that is partially offset by any excess in total travel distance necessary to use the toll or HOV facility. The additional preference for toll and HOV facilities can be explained by a perception of lower travel time, less driving stress, and higher reliability on these facilities. These results suggest that selection of a least-cost path in trip assignment is not sufficient for modeling the use of toll and HOV facilities.

Advanced Practices in Travel Forecasting

This study explores the use of travel modeling and forecasting tools that represent significant advances over the current state of practice. The study includes five types of models: activity-based demand, dynamic network, land use, freight, and statewide. Information was gathered through literature review; detailed interviews among federal, state, and metropolitan agencies, and consulting firms; and case studies.

Market-Based Framework for Forecasting Parking Cost in Traditional and Microsimulation Modeling Applications

Parking cost is an important variable in determining mode choice, yet it receives little attention in most travel forecasting models. This paper presents a framework for modeling parking supply and cost that has three advantages over most parking cost models: a market-based approach is used to equilibrate parking demand with parking supply; actual parking costs paid by groups of travelers rather than average parking costs are estimated for each transportation analysis zone; and estimates are made from longitudinal data. This framework has been applied successfully in a traditional four-step travel model and is being used in practice. It also provides additional opportunities for application in a segmented manner or in concert with a microsimulation modeling approach. Mode choice results based on aggregate and segmented applications of the framework are substantially different. Improved forecasting of parking costs should be an important consideration in any new model development. In recent years, substantial efforts have been focused on household interactions and activity modeling. Although the understanding of travel behavior has improved substantially, the improved techniques still depend on good input data for credible forecasts.

Estimating Emissions Benefits of Bicycle Facilities with Stand-Alone Software Tools: Incremental Nested Logit Analysis of Bicycle Trips in California’s Monterey Bay Area

To improve their capability to estimate the emissions reduction benefits of new bicycle facilities, the Association of Monterey Bay Area Governments and the Monterey Bay Unified Air Pollution Control District in California sought to develop a new software tool. The tool was required not only to deliver accurate forecasts with a small development budget but also to operate independently of other models, run on a platform that could be freely distributed without special software licenses, and provide user-friendly access to planners in multiple agencies with varying levels of technical skill. For these requirements to be satisfied, an incremental nested logit mode choice model was developed that pivoted off static exports of trip tables from the Association of Monterey Bay Area Governments’ existing four-step travel model. GPS traces collected from smartphone users in the region were analyzed to estimate a path-size logit route choice model, and the California Household Travel Survey was used to estimate a scaling coefficient on the best route utility. The model was implemented in an Adobe ActionScript graphical user interface. After the user edits the bicycle network in the user interface, new bicycle levels of service are skimmed, and new shares for each mode are calculated from their original shares in the no-build alternative and the change in the bicycle level of service. Finally, the emissions reduction is estimated on the basis of the distance and average speed of the vehicle trips replaced by bicycle travel. The result is an accurate, fast, freely distributable, user-friendly tool that is consistent with the forecasts produced by the four-step model.