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.

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