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.

Incorporating Autonomous Vehicles in the Traditional Four-Step Model

“…Although the consideration of AVs in practice is limited, there have been some research-based studies using trip-based modeling, including refining the traditional gravity model to enable lower sensitivity to travel time in the trip distribution step and considering AVs as a distinct mode in the mode choice stage. There are some very recent examples that implement small refinements on a trip-based framework at the statewide and metropolitan scales in Texas, Toronto (Canada), Michigan, Illinois, Virginia, Indiana, South Carolina, and Ontario (Canada) ( 8 – 11 ). However, these studies typically take a simplified approach when modeling the capacity consumption of AVs, often using a single network-wide factor to adjust link-level capacities or simply occasionally ignoring capacity impacts altogether.…”

The Role of Urban Form in the Performance of Shared Automated Vehicles

“…Some studies also suggest that SAVs may lead to congestion problems (Levin et al, 2017;Zhao and Kockelman, 2018). Therefore, several Metropolitan Planning Organizations (MPOs) have already recognized the necessity to incorporate AVs in long-range plans to harvest the benefits brought by AVs, while curbing their adverse effects (Childress et al, 2015;Bernardin Jr et al, 2019;Kim et al, 2015). However, decision-makers face several challenges when incorporating SAVs into the planning process.…”

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

“…The two methods have been used to explore long-term changes in travel-related behavior such as residential and work location choices and short-term changes such as daily activity patterns. For instance, simulation studies consistently find that the introduction of (shared) AVs will lead to an increase in vehicle miles traveled (VMT) (e.g., Childress et al [ 4 ]; Taiebat et al [ 5 ]), the number of vehicle trips (e.g., Bernardin et al [ 6 ]; Vyas et al [ 7 ]), and the average trip length (e.g., Auld et al [ 8 ]; Thakur et al [ 9 ]). Moreover, the literature indicates that AV options are likely to cannibalize transit ridership (e.g., Kröger et al [ 10 ], World Economic Forum [ 11 ]), largely owing to the assumption made on the reduction of AV riders’ value of time, which are backed by findings from survey studies (e.g., Malokin et al [ 12 ]; Zhong et al [ 13 ]).…”