Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 3
Abstract
The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from the next generation simulation (NGSIM) and Lyft Level 5 (Lyft L5) prediction data sets to investigate whether the existence of AVs influences the car-following behavior of human drivers. We use time headway time series as a proxy to capture the car-following behavior of human drivers. We then develop a nested fixed model to find possible changes in behavior when human drivers are following different types of vehicles (i.e., human-driven vehicles or AVs). The factors included in this model are the platoon structure (a legacy vehicle following a legacy vehicle, and a legacy vehicle following an autonomous vehicle), road type (freeway and urban), time period (morning and afternoon), lane (right, middle, and left), and the source of the data (NGSIM and Lyft L5). Results indicate a statistically significant difference between the car-following behavior of drivers when they follow a human-driven vehicle compared to an AV. This change in the car-following behavior of drivers is manifested in the form of a reduction in the mean and variance of time headways when human drivers follow an AV. These findings can bridge the gap between anticipated and real-world impacts of AVs on traffic streams and roadway capacity, providing meaningful insights on the influence of AVs on the driving behavior of humans in a naturalistic driving environment.
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Data Availability Statement
Some of models, or code that support the findings of this study are available from the corresponding author upon reasonable request; All data used during the study are available in repositories online in accordance with the funder’s data retention policies.
Acknowledgments
This work has been supported by Midwest US-DOT Center for Connected and Automated Transportation (Award No. 69A3551747105) and National Science Foundation (Award No. 1837245).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 3 • March 2023
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© 2022 American Society of Civil Engineers.
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Received: Feb 26, 2022
Accepted: Oct 27, 2022
Published online: Dec 19, 2022
Published in print: Mar 1, 2023
Discussion open until: May 19, 2023
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