Cooperative control of dynamic CAV dedicated lanes and vehicle active lane changing in expressway bottleneck areas

Yunran Di, Weihua Zhang, Heng Ding, Xiaoyan Zheng and Bin Ran

Physica A: Statistical Mechanics and its Applications, 2024, vol. 638, issue C

Abstract: Bottleneck areas on expressways plague the operational efficiency of entire road systems. In mixed traffic flow environments consisting of connected and autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), it is believed that road capacity can be improved to relieve traffic congestion in bottleneck areas by setting CAV dedicated lanes (CDLs) on expressways. Existing static CDL setup methods provide fixed exclusive right-of-way for CAVs but cannot accommodate dynamic changes in traffic demand. To address this issue, utilizing lane control signals and CAV active lane-changing technology, a cooperative method involving dynamic CAV dedicated lane control (CDLC) and active lane-changing control (LCC) in the bottleneck areas of expressways is proposed in this paper. First, a fundamental diagram of traffic flow with CDLs is established based on the microscopic car-following model of mixed traffic flow, which can be used to determine the conditional thresholds for setting the CDLs of multilane expressways. Second, an expressway traffic model with CDLs is established by improving the lane-level cell transmission model. Finally, cooperative control of dynamic CAV dedicated lanes and active lane changing (CDLLC) is proposed based on the model predictive control (MPC) framework for solving the congestion problem at expressway bottlenecks in a mixed driving environment. The simulation results show that the proposed CDLLC strategy can effectively reduce the total vehicle travel time and decrease the risk of congestion propagation at expressway bottlenecks when compared to the LCC-only strategy.

Keywords: Expressway bottleneck; Dynamic CAV dedicated lane; Lane changing control; Model predictive control (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:638:y:2024:i:c:s0378437124001316

DOI: 10.1016/j.physa.2024.129623

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