华南理工大学学报(自然科学版) ›› 2025, Vol. 53 ›› Issue (8): 11-19.doi: 10.12141/j.issn.1000-565X.240362

• 智慧交通系统 • 上一篇    下一篇

城市轨道交通限流背景下的协作疏解研究

王豹, 罗霞, 乔璇, 苏启明   

  1. 西南交通大学 交通运输与物流学院,四川 成都 611756
  • 收稿日期:2024-07-15 出版日期:2025-08-25 发布日期:2025-01-17
  • 通信作者: 罗霞(1962—),女,教授,博士生导师,主要从事智能交通研究。 E-mail:xia.luo@263.net
  • 作者简介:王豹(1995—),男,博士生,主要从事城市轨道交通客流预测与管控研究。E-mail: wangbao@my.swjtu.edu.cn
  • 基金资助:
    四川省科技计划项目(2020YJ0255)

Research on Collaborative Transfer Under the Condition of Urban Rail Transit Passenger Flow Control

WANG Bao, LUO Xia, QIAO Xuan, SU Qiming   

  1. Southwest Jiaotong University,School of Transportation and Logistics,Chengdu 611756,Sichuan,China
  • Received:2024-07-15 Online:2025-08-25 Published:2025-01-17
  • Contact: 罗霞(1962—),女,教授,博士生导师,主要从事智能交通研究。 E-mail:xia.luo@263.net
  • About author:王豹(1995—),男,博士生,主要从事城市轨道交通客流预测与管控研究。E-mail: wangbao@my.swjtu.edu.cn
  • Supported by:
    the Science and Technology Project of Sichuan Province(2020YJ0255)

摘要:

针对现阶段对城市轨道交通网络限流场景下的被限制客流转运关注不足等问题,对特定限流条件下转运车辆线路布设和运力配置进行研究。首先,分析量化不同转运线路条件下,乘客选择乘坐转运车辆的效用和选择概率。在此基础上,以最小化总期望旅行时间、最小化转运车辆开行成本、最大化轨道交通网络拥堵区间客流疏解量为目标,构建了限流场景下转运车辆线路设计和运力规划模型。为提高模型求解效率,将模型拆解为开行路径优化和开行方案优化两个子问题,把第1个子问题转换为旅行商问题,将获得的备选线路路径作为第2个子问题的输入进行模型求解。依托成都市轨道交通网络和早高峰时段的客流数据,验证不同限流强度下所提模型的有效性,讨论了转运线路对停站点数量和停靠站点选取的偏好。结果表明:目标值表现良好的线路多为停站点为2-3个的路径,在停靠站点的选取上高度集中,对3-4个特定路径的偏好显著;随着限流强度的逐步加强,偏好于选择停站数量较少、走行距离较短的线路以达到快速转运的需求,在开行班次的选取上,整体上呈现线性增长趋势,但当限流强度大于0.8时,单一线路难以满足转运需求,不再保持线性增长趋势。

关键词: 综合运输, 客流转运, 协同优化, 地铁客流, 客流控制, 旅行商问题

Abstract:

To address the current lack of attention to the transfer of restricted passenger flows under urban rail transit network flow control scenarios, this study investigated the routing and capacity allocation of transfer vehicles under specific flow control conditions. Firstly, the utility and selection probability of passengers opting for transfer vehicles were analyzed and quantified across various route conditions. Then, a model for the design of transfer bus routes and capacity planning under flow control scenarios was proposed, aiming to minimize total expected travel time and the operational costs of transfer vehicles, and maximize the alleviation of passenger congestion in the rail transit network. To enhance model-solving efficiency, the model was divided into two subproblems: route optimization and service optimization. The first subproblem was transformed into a traveling salesman problem, with the resulting alternative route paths serving as input for solving the second subproblem. Based on Chengdu’s urban rail transit network and passenger flow data during the morning peak period, the effectiveness of the proposed model under different levels of flow restrictions was verified, and the preferences for the number of stops and the selection of transfer station locations were discussed. Results indicate that routes with 2 to 3 stops generally perform well in terms of the objective function, and the selection of stopping stations is highly concentrated, with a strong preference for 3 to 4 specific routes. As flow control intensity increases, there is a clear tendency to choose routes with fewer stops and shorter travel distances to meet rapid transfer demands. The number of scheduled trips increases approximately linearly overall; however, when the flow restriction intensity exceeds 0.8, a single route can no longer meet the transfer demand, and the linear growth trend no longer holds.

Key words: integrated transportation, passenger transit, collaborative optimization, metro passenger flow, passenger flow control, traveling salesman problem

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