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面向新型混合交通流的智能交叉口网络布局优化
引用本文:李同飞,曹雅宁,窦雪萍,熊杰,许琰,周文涵. 面向新型混合交通流的智能交叉口网络布局优化[J]. 交通运输系统工程与信息, 2022, 22(4): 302-312. DOI: 10.16097/j.cnki.1009-6744.2022.04.034
作者姓名:李同飞  曹雅宁  窦雪萍  熊杰  许琰  周文涵
作者单位:北京工业大学,交通工程北京市重点实验室,北京 100124
基金项目:国家自然科学基金;北京市自然科学基金
摘    要:考虑网联自动驾驶车辆(Connected Autonomous Vehicle, CAV)应用先进的车联网与自动驾驶技术,可以采用智能交叉口的组织形式,大幅提升交叉口的通行效率,为降低CAV与人工驾驶车辆(Human-driven Vehicle, HV)混行条件下城市交通系统的整体出行成本,提出智能交叉口在城市交通网络中的布局优化问题,建立数学优化模型并求解。首先,基于对两类车辆行驶特性的分析,建立混合用户均衡模型,描述CAV与HV的路径选择行为;其次,从交通规划者的角度,以系统最优为目标,整合混合用户均衡模型,建立面向新型混合交通流的智能交叉口网络布局优化模型,并利用改进的遗传算法求解;最后,选取Sioux-Falls交通网络作为案例分析,验证模型与算法的有效性,并研究CAV渗透率变化对优化结果的影响。研究表明,智能交叉口在城市路网中的合理规划极大地提高了新型混行场景下城市交通系统的出行效率,同时,大幅降低了由于网联自动驾驶单方面技术优势带来的CAV与HV的出行效率差距,增进了出行公平性。

关 键 词:城市交通  智能交叉口网络布局规划  含均衡约束的数学规划  网联自动驾驶  新型混合交通流  混合用户均衡  
收稿时间:2021-11-16

Layout Optimization of Smart Intersections UnderNovel Mixed Traffic Flow
LI Tong-fei,CAO Ya-ning,DOU Xue-ping,XIONG Jie,XU Yan,ZHOU Wen-han. Layout Optimization of Smart Intersections UnderNovel Mixed Traffic Flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 302-312. DOI: 10.16097/j.cnki.1009-6744.2022.04.034
Authors:LI Tong-fei  CAO Ya-ning  DOU Xue-ping  XIONG Jie  XU Yan  ZHOU Wen-han
Affiliation:Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
Abstract:Due to the advanced Internet of vehicles and autonomous driving technology, connected autonomousvehicles (CAVs) can adopt a new mode of traffic organization (i.e., smart intersections) to significantly improve theefficiency of intersections. To reduce the total travel cost of urban traffic systems under mixed CAVs and human-drivenvehicles (HVs), the layout optimization problem of smart intersections in the urban traffic network is proposed. Amathematical optimization model is established and used to solve the problem. First, based on the analysis of thedriving characteristics of the two types of vehicles, a mixed user equilibrium model is established to formulate the pathchoice behavior of CAVs and HVs. Second, from the perspective of traffic planners, with the integration of a mixeduser equilibrium model, a spatial layout optimization model of smart intersections is established for the urban trafficnetwork under the novel mixed traffic flow. It takes system optimization as the optimization objective and is solved byan improved genetic algorithm. Finally, a set of numerical experiments based on the Sioux-Falls network is conductedto verify the validity of the model and algorithm. Besides, the influence of the penetration rate of CAVs on theoptimization results is also analyzed. The results show that the rational planning of smart intersections in the urbantraffic network can significantly improve the travel efficiency in the novel mixed traffic scenario. Moreover, it greatlyreduces the gaps in travel efficiency between CAVs and conventional HVs due to CAVs' unilaterally technologicaladvantages, which further improves traffic fairness.
Keywords:urban traffic   layout planning of smart intersections   mathematical programming with equilibriumconstraints   connected autonomous vehicle   novel mixed traffic flow   mixed user equilibrium  
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