交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (5): 222-227.

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基于双层规划的城市交通拥塞疏导优化研究

胡立伟*,赵雪亭,杨锦青,张苏航,范仔健,殷秀芬,郭治   

  1. 昆明理工大学,交通工程学院,昆明 650500
  • 收稿日期:2021-01-09 修回日期:2021-01-31 接受日期:2021-02-02 出版日期:2021-10-25 发布日期:2021-10-21
  • 作者简介:胡立伟(1978- ),男,山东潍坊人,教授,博士。
  • 基金资助:
    国家自然科学基金

Urban Traffic Congestion Mitigation and Optimization Study Based on Two-level Programming

HU Li-wei* , ZHAO Xue-ting, YANG Jin-qing, ZHANG Su-hang, FAN Zi-jian, YIN Xiu-fen, GUO Zhi   

  1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Received:2021-01-09 Revised:2021-01-31 Accepted:2021-02-02 Online:2021-10-25 Published:2021-10-21
  • Supported by:
    National Natural Science Foundation of China

摘要: 为研究城市路网区域信号控制下的交通子区划分及交通拥塞疏导优化问题,明确交通拥 塞控制子区划分影响因素并建立相邻交叉口关联度模型,将拥塞控制区域划分为“疏散区”“平衡 区”。考虑“疏散区”“平衡区”不同的优化目标构建城市路网交通拥塞疏导优化双层规划模型,并 以昆明市部分城市路网作为实验对象,通过仿真将实际控制方案与本文模型计算方案的控制效 果进行对比,以此验证模型的适用性和有效性。仿真结果表明:除两个方案的平均停车次数优化 效果一般以外,其他控制效果指标均具有很大的优化提升,其中车辆平均延误时间降低了7.7 s, 重度拥塞里程比例下降了10.1%,路网车辆占有率下降了14.1%。综上可知,本文的疏导优化模 型控制可快速疏导和有效缓解交通拥塞,模型具有良好的有效性,可为路网交通拥塞分区治理及 疏导控制提供参考。

关键词: 城市交通, 拥塞疏导优化模型, 双层规划理论, 城市交通拥塞, 交通小区划分, 关联度

Abstract: This paper focuses on the traffic zoning and traffic congestion optimization in urban road network under regional signal control. The factors that determines the traffic zones under congestions are identified and a model is proposed to describe the correlation degree of adjacent intersections. The congestion control sub-area is divided into “evacuation zone”and“balance zone”. Considering the different optimization objectives for“evacuation zone”and “balance zone”, the paper develops a bi-level optimal programming model for urban road network congestion mitigation. Taking some part of the road networks in Kunming city as an example, the paper compares the effectiveness of the actual control scheme and the proposed model through simulations. The results show that the proposed model performs well in the congestion control, except two optimization schemes on the average number of stops. The average vehicle delay time is reduced by 7.7 s, the proportion of severely congested mileage is reduced by 10.1% , and the vehicle occupancy in the network decreases 14.1%. The model has good effectiveness and can provide references for road network traffic congestion zoning and mitigation.

Key words: urban traffic, congestion mitigation and optimization model, bi-level programming, urban traffic congestion, traffic zoning, correlation degree

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