Developing a Variable Speed Limit Control Strategy for Mixed Traffic Flow Based on Car-Following Collision Avoidance Theory

Chen Yuan, Yuntao Shi, Bin Pan and Ye Li ()
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Chen Yuan: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Yuntao Shi: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Bin Pan: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Ye Li: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Mathematics, 2022, vol. 10, issue 16, 1-18

Abstract: Variable speed limit (VSL) control is an effective technology to improve safety near freeway bottlenecks. This study aims to develop a control strategy for mixed traffic flow consisting of both human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) based on collision avoidance theory. A microscopic simulation platform is first established, and four vehicle longitudinal dynamic models including Cruising model, Intelligent Driver Model (IDM), Adaptive Cruise Control model (ACC), Cooperative Cruise Control model (CACC) and one vehicle lateral dynamic model Minimizing Overall Braking Induced by Lane Changes model (MOBIL) are incorporated into the simulation platform. Then, a new VSL control strategy derived from collision avoidance theory is proposed for mixed traffic flow at the initial stage of CAVs’ popularization. Extensive simulation experiments are conducted, and surrogate safety measures and total travel time indicators are utilized to evaluate the safety and efficiency performances of the proposed VSL control. Results indicate that the proposed VSL control strategy can effectively improve the safety performance near freeway bottlenecks with an acceptable efficiency level.

Keywords: freeway bottleneck; connected and automated vehicles; variable speed limit; safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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