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智能网联车环境下交叉口车流轨迹优化模型
引用本文:高志波,吴志周,郝威,杨玥,龙科军,邹清全. 智能网联车环境下交叉口车流轨迹优化模型[J]. 交通运输系统工程与信息, 2021, 21(2): 91-97. DOI: 10.16097/j.cnki.1009-6744.2021.02.013
作者姓名:高志波  吴志周  郝威  杨玥  龙科军  邹清全
作者单位:1. 同济大学,道路与交通工程教育部重点实验室,上海 201804;2. 长沙理工大学,智能道路与车路协同湖南省重点实验室,长沙 410004;3. 上海汽车集团股份有限公司,上海 201804
基金项目:国家自然科学基金/National Natural Science Foundation of China (61773288, 51678076);上海汽车工业科技发展基金/Shanghai Automobile Industry Technology Development Fund(1916)。
摘    要:在智能网联环境下,车辆可通过相互穿插和协作通过交叉口,无需信号灯控制.为保证车辆安全高效运行,建立车辆到达时序和速度协同优化的交叉口车流轨迹优化模型.提出车辆到达时序优化模型和车辆速度优化模型,建立车辆到达时刻与速度的函数关系;在此基础上,模型以所有车辆在控制区域的行程时间与油耗加权最小为目标,车辆路径、到达时刻和速度...

关 键 词:城市交通  智能网联车  交叉口  时序优化  轨迹优化
收稿时间:2020-07-16

Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment
GAO Zhi-bo,WU Zhi-zhou,HAO Wei,YANG Yue,LONG Ke-jun,ZOU Qing-quan. Vehicle Trajectory Optimization Model for Intersection under the Connected and Automated Vehicles Environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 91-97. DOI: 10.16097/j.cnki.1009-6744.2021.02.013
Authors:GAO Zhi-bo  WU Zhi-zhou  HAO Wei  YANG Yue  LONG Ke-jun  ZOU Qing-quan
Affiliation:1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; 2. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure System, Changsha University of Science and Technology, Changsha 410004, China; 3. SAIC Motor Corp. LTD., Shanghai 201804, China
Abstract:Under the connected and automated driving environment, vehicles can cross the intersection with good coordination and minimal controls from traditional traffic signals. To ensure the safe and efficient vehicle operations at intersections, this study proposes a trajectory optimization model to optimize vehicle arrival time and speed. The vehicle arrival time sequence optimization model and the vehicle speed optimization model are developed to establish the functional relation between vehicle arrival time and speed. Then, the weighted sum of all vehicle travel time and fuel consumptions are set as the objective of the proposed model. The decision variables include vehicle route, arrival time, and speed. An iterative algorithm is designed to optimize both the vehicle arrival time and speed, and maximize the operation benefit at the intersection. Compared with the results from the two-level trajectory optimization model, the proposed model reduced the average delay by 32.0% and reduced the fuel consumption by 9.9% . The proposed model has good flexibility and mobility, which can reduce both vehicle delays and fuel consumptions.
Keywords:urban traffic  connected and automated vehicles  intersection  time sequence optimization  trajectory optimization  
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