Managing merging from a dedicated CAV lane into a conventional lane considering the stochasticity of connected human-driven vehicles

Guohong Wu, Jiaming Wu, Shiteng Zheng and Rui Jiang

Physica A: Statistical Mechanics and its Applications, 2024, vol. 652, issue C

Abstract: Connected and automated vehicle (CAV) provides a new promising solution for transportation system. Despite the promising future of CAV, fully deployment of CAV on current road systems is still challenging and the coexistence of CAV and human-driven vehicle (HDV) is inevitable. Furthermore, most studies for trajectory planning under mixed traffic ignore the stochasticity of human-driven vehicle (HDV), which is unrealistic and causes infeasible planned trajectory. In this study, we investigate merging control from a dedicated CAV lane into a conventional lane. The stochastic mixed traffic cooperative merging problem is formulated as a mixed integer quadratic constraint programming, which optimizes vehicle longitudinal trajectories and lane-changing maneuvers in a centralized way. Rolling horizon framework coupled with car-following and lane-changing execution algorithms is used to address the stochasticity of connected human-driven vehicle (CHV). Simulation results validate our proposed control strategy outperforms the rule-based control strategy from the perspective of traffic efficiency, lane-changing efficiency, fuel economy and driving comfort. The robustness of rolling horizon framework and sensitivity analysis are also conducted. Finally, the vehicle trajectory comparison intuitively shows the difference between 2 control strategies.

Keywords: Connected and automated vehicle; Merging control; Stochastic mixed traffic flow; Trajectory planning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:652:y:2024:i:c:s0378437124005533

DOI: 10.1016/j.physa.2024.130044

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