It is common knowledge that travelers often choose clusters or groups of nearby destinations that can be visited conveniently in a single tour. This propensity is becoming increasingly important in the context of rising fuel costs. However, gravity models, as well as most destination choice models, ignore these agglomeration effects and treat each trip or destination choice as independent. Some models have captured economies of agglomeration related to trip chaining through the use of accessibility variables. Accessibility variables, however, generally do not identify trip chaining effects uniquely, but measure differential spatial competition that arises because nearby destinations are generally better substitutes than distant ones. Because spatial competition effects generally dominate trip chaining agglomeration effects, models with a single accessibility variable have been called competing destinations models following Fotheringham. This paper presents an advance on Fotheringham's approach by introducing two distinct accessibility variables to represent agglomeration and spatial competition among destinations separately rather than their net effect. These new agglomerating and competing destination choice models were applied in Knoxville, Tennessee. The new models, which outperformed both gravity and competing destinations models, began to present a new alternative to activity-based models by allowing the incorporation of some of the most important trip chaining effects in trip-based travel demand models. For example, a sensitivity test showed that a new factory employing 1,000 workers would attract 125 new nonwork trips to the surrounding area on an average day as a result of stops on the way to and from work.

Discrete choice models, Mode choice, Destination choice, Demand, Elasticity, Hierarchical nesting, Integrated models,

The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.

While a range of methods have been employed to quantify certain anticipated impacts of connected and autonomous vehicles (CAVs), a comprehensive framework for integrating CAVs into trip-based models, like those used by many metropolitan areas today, is lacking. Without real-world CAV usage data, integrating CAVs into trip-based models today requires speculative modeling assumptions; however, incorporating fundamental parameters into existing travel modeling frameworks is timely for two reasons. First, understanding the range of possible futures from scenario planning or exploratory modeling analysis can assist metropolitan areas anticipate and manage the potential risks and benefits of CAVs. Second, data on the travel behavior of early CAV adopters will become available during the lifespan of many models currently in use or development. This paper summarizes an enhanced trip-based modeling framework incorporating uncertainties related to CAVs initially developed in support of the Michigan Department of Transportation’s statewide model. This framework is now being applied in statewide and metropolitan scale models in Michigan, Illinois, Virginia, Indiana, and South Carolina. An important contribution of this framework is its typology of and methods for representing zero-occupant vehicle (ZOV) trips. Additionally, this paper details an exploratory analysis of CAV scenarios in Vermont using a trip-based model incorporating several elements of the framework. In this application, reasonable assumptions related to induced CAV demand, including ZOV trips, resulted in substantial increases in vehicle miles traveled, vehicle hours traveled, and delay despite capacity increases, demonstrating how relatively basic trip-based scenario modeling of CAVs can be a valuable tool for informing and encouraging public policy discussions.

This paper presents the successful application of a new method to improve travel demand forecasting models by taking advantage of cheap and readily available traffic count data and using them together with household travel survey data to inform the model's parameter estimates. Although traffic counts are frequently used in an ad hoc manner in the validation of travel model components, this paper presents a more rigorous, structured, and statistically efficient method to allow the information contained in traffic counts to influence the selection of model parameters simultaneously with household survey data. This formal process allows traffic counts to inform indirectly, but importantly, related parameters, such as destination choice utility functions, through formal statistical inference when human inference would be difficult, if not impossible, because of the complexity of the system and when manual random trial and error would be time- and cost-prohibitive. The approach used a genetic algorithm metaheuristic to implement a composite log likelihood and a pseudo composite log likelihood maximization in the development of a new travel model for the South Bend, Indiana, urban area for the Michiana Area Council of Governments. The process made use of a set of parameters transferred from another region and resulted in new parameters that produced significantly better consistency in the model with both local survey data and counts. Although computationally intense, this exciting new approach showed promise, at least for midsized urban areas.