Optimizing Freeway Merge Operations under Conventional and Automated Vehicle Traffic
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 7
Abstract
This paper presents an optimization algorithm for freeway operations at merge zones that maximizes the average speed of the segment in the presence of connected and automated vehicles (CAVs) and human-operated (i.e., conventional) vehicles. This research assumes that CAVs have the capability to communicate with each other and with the infrastructure and to execute the recommended trajectories. The proposed system receives arrival information as input and generates optimal trajectories for CAVs while predicting the behavior of conventional vehicles and accounting for deviation from expected behavior. The necessary algorithms are developed to simulate and carry out the merging operations on a two-lane freeway (one mainline and one ramp lane) and tested under a variety of scenarios considering demand level, demand splits, and CAV penetration rate. Results suggest that the proposed algorithm can efficiently manage the traffic at freeway merge zones and reduce the average total travel time (or increase average speed). The results indicate that a minimum of 25% CAV penetration rate is required to observe improvements in operational conditions.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This material is based upon work supported by NSF Award 1446813, CPS: TTP Option: Synergy: Traffic Signal Control with Connected and Autonomous Vehicles in the Traffic Stream.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jun 21, 2019
Accepted: Jan 3, 2020
Published online: May 4, 2020
Published in print: Jul 1, 2020
Discussion open until: Oct 4, 2020
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