北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (7): 1349-1360.doi: 10.13700/j.bh.1001-5965.2018.0613

• 论文 • 上一篇    下一篇

车路协同系统下区域路径实时决策方法

王庞伟1, 邓辉1, 于洪斌2, 李振华1,3, 王力1   

  1. 1. 北方工业大学 城市道路交通智能控制技术北京市重点实验室, 北京 100144;
    2. 中国公路工程咨询集团有限公司 中咨数据有限公司, 北京 100089;
    3. 交通运输部公路科学研究院 智能交通技术交通运输行业重点实验室, 北京 100088
  • 收稿日期:2018-10-23 出版日期:2019-07-20 发布日期:2019-07-25
  • 通讯作者: 王庞伟 E-mail:wpw@ncut.edu.cn
  • 作者简介:王庞伟 男,博士,副教授。主要研究方向:车路协同控制系统、车联网与智能驾驶技术;邓辉 女,硕士研究生。主要研究方向:车路协同控制系统;于洪斌 男,硕士,助理研究员。主要研究方向:交通大数据、交通规划;李振华 男,博士研究生,高级工程师。主要研究方向:车路协同系统、交通大数据分析;王力 男,博士,教授。主要研究方向:智能交通系统、交通信号控制。
  • 基金资助:
    国家重点研发计划(2018YFB1600504);国家自然科学基金(61603004);北京市自然科学基金(4174088);北京市科技新星计划(Z181100006218076);北方工业大学毓杰人才支持计划(18XN154-003)

Real-time regional path decision method in cooperative vehicle infrastructure system

WANG Pangwei1, DENG Hui1, YU Hongbin2, LI Zhenhua1,3, WANG Li1   

  1. 1. Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China;
    2. China Highway Engineering Consultants Corporation Data Co., Ltd., China Highway Engineering Consultants Corporation, Beijing 100089, China;
    3. Transportation Industry Key Lab of Intelligent Traffic Technology, Research Institute of Highway Ministry of Transport, Beijing 100088, China
  • Received:2018-10-23 Online:2019-07-20 Published:2019-07-25
  • Supported by:
    National Key R & D Program of China (2018YFB1600504); National Natural Science Foundation of China (61603004); Beijing Natural Science Foundation (4174088); Beijing Nova Program (Z181100006218076); Yujie Talent Support Project of NCUT (18XN154-003)

摘要: 为解决车辆行驶数据缺失和滞后造成路径规划系统不稳定问题,建立了基于车路协同系统(CVIS)的新型区域路径实时决策方法。首先,通过获取网联车辆的实时行驶数据,结合交通信号配时和路径转向信息,并考虑车辆在途经交叉口时可能遇到的非自由流行驶情况,动态计算当前路段路阻值;其次,根据当前时刻各路段的路阻统计数据,以及区域路网拓扑结构,实时预测各备选路线的行程时间,选择行程时间最少的路线作为车辆最优行驶路径;最后,选取北京市望京地区的典型区域路网数据进行验证。在150组实验过程中,计算得出不同时段下按所提方法得到的最优路线用时平均比常规导航系统推荐最优路线用时分别短9.52 s、13.39 s及20.65 s,证明了所提方法的有效性。

关键词: 智能交通系统, 车路协同系统(CVIS), 实时路径决策, 网联汽车, 城市交通诱导

Abstract: To solve the instability of the path planning system caused by vehicle driving data loss and lag, a novel real-time regional path decision method based on the cooperative vehicle infrastructure system (CVIS) was presented in this paper. Firstly, the current road section resistance value was calculated dynamically through acquiring the real-time driving data of connected vehicles, combing with the traffic signal timing and path steering information, and considering the non-free flow situation which vehicles may encounter when passing through the intersection. Secondly, the travel time of each alternative route was predicted in real time according to the current road resistance statistics and the road network topology structure. After that, the predicted route with the least travel time was selected as the optimal vehicle driving path. Finally, the typical regional road network data of Wangjing area in Beijing was selected as the test scenario. 150 sets of tested results show that the average travel time in different periods of the optimized route obtained by this method is 9.52 seconds, 13.39 seconds and 20.65 seconds shorter than recommended route of the navigation system respectively, which proves the feasibility of the proposed method.

Key words: intelligent transportation system, cooperative vehicle infrastructure system (CVIS), real-time path decision, connected vehicles, urban traffic guidance

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