Scenario Modeling of Autonomous Vehicles with Trip-Based Models

Abstract

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.