引用本文:王云鹏,郭戈.城市交叉口车辆速度与交通信号协同优化控制[J].控制与决策,2019,34(11):2397-2406
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城市交叉口车辆速度与交通信号协同优化控制
王云鹏1, 郭戈2,3
(1. 大连理工大学控制科学与工程学院,辽宁大连116024;2. 东北大学流程工业综合自动化国家重点实验室,沈阳110819;3. 东北大学秦皇岛分校控制工程学院,河北秦皇岛066004)
摘要:
为了降低城市交通中的行车延误与燃油消耗,针对人类驾驶车辆与自动驾驶车辆混合交通环境,提出一种基于交通信息物理系统(TCPS)的车辆速度与交通信号协同优化控制方法.首先,综合考虑路口交通信号、人类驾驶车辆、自动驾驶车辆三者之间的相互影响,设计一种适用于自动驾驶车辆与人类驾驶车辆混合组队特性的过路口速度规划模型;其次,针对车辆速度规划单一应用时的局限性,即无法减少车辆路口通行延误且易出现无解情况,提出一种双目标协同优化模型,能够综合考虑车辆速度规划与路口交通信号控制,同时降低车辆燃油消耗与路口平均延误.由于双目标优化问题求解的复杂性,设计一种遗传算法-粒子群算法混合求解策略.基于SUMO的仿真实验验证了所提出方法的有效性.
关键词:  速度规划  交通控制  绿色驾驶  交通信息物理系统  双目标优化  混合交通
DOI:10.13195/j.kzyjc.2019.0581
分类号:TP273
基金项目:国家自然科学基金项目(61573077);国家自然科学基金重点项目(U1808205).
Joint optimization of vehicle speed and traffic signals at a signalized intersection
WANG Yun-peng1,GUO Ge2,3
(1. School of Control Science and Engineering,Dalian University of Technology,Dalian116024,China;2. State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang110819,China;3. School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao066004,China)
Abstract:
For the reducing of traffic delay and fuel consumption in urban traffic, a joint optimization method of vehicle speed and traffic signals based on transportation cyber physical systems(TCPS) is proposed for the mixed traffic environment of human-driven vehicles and autonomous vehicles. Firstly, considering the interaction among traffic signals, human-driven vehicles and autonomous vehicles, a speed planning model is developed suitable for the mixed group of human-driven vehicles and autonomous vehicles. Then, aiming at the limitation of vehicle speed planning in application, i.e., unable to reduce the vehicle delay and easy to occur no solution phenomenon, a bi-objective optimization model is proposed. Vehicle speed planning and traffic signal control are comprehensively integrated to meet the simultaneous reduction of vehicle fuel consumption and delay. For the complexity of the problem, a hybrid intelligent algorithm merging the genetic algorithm and the particle swarm optimization algorithm is designed. Finally, simulation experiments based on SUMO show the effectiveness of the proposed method.
Key words:  speed planning  traffic control  eco-driving  transportation cyber physical systems(TCPS)  bi-objective optimization  mixed traffic

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