Traffic signal coordination control for arteriais with dedicated CAV lanes

Liang Xu;Sheng Jin;Bolin Li;Jiaming Wu
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引用次数: 7

Abstract

Purpose - This study aims to make full use of the advantages of connected and autonomous vehicles (CAVs) and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping. Design/methodology/approach - The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming. CAV non-stop constraints are proposed to adapt to the characteristics of CAVs. As it is a continuous problem, various situations that CAVs arrive at intersections are analyzed. The rules are discovered to simplify the problem by discretization method. Findings - A case study is conducted via SUMO traffic simulation program. The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper. At the same time, the progression efficiency of regular vehicles is not affected significantly. It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics. Originality/value - To the best of the authors' knowledge, this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.
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CAV专用车道的大动脉交通信号协调控制
目的——本研究旨在充分利用互联自动驾驶汽车(CAV)和专用CAV车道的优势,确保所有CAV都能通过十字路口而不停车。设计/方法/方法-作者通过使用混合整数线性规划,开发了具有专用CAV车道的动脉的信号协调模型。为了适应CAV的特点,提出了CAV不间断约束。由于这是一个连续的问题,分析了CAV到达交叉口的各种情况。通过离散化方法发现了简化问题的规则。调查结果-通过SUMO交通模拟程序进行了案例研究。结果表明,采用本文提出的模型优化方案,CAV在大容量和中容量情况下都能显著提高效率。同时,普通车辆的行驶效率没有受到显著影响。研究表明,只有考虑CAV特性的信号协调计划才能实现专用CAV车道的全面效益。独创性/价值-据作者所知,这是第一项为具有专用CAV车道的动脉开发信号协调模型的研究。
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Front Cover Contents Advancements and Prospects in Multisensor Fusion for Autonomous Driving Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip
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