事件干扰下的城市轨道交通客流诱导优化研究

doi:10.16097/j.cnki.1009-6744.2024.06.007

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (6): 76-85.DOI: 10.16097/j.cnki.1009-6744.2024.06.007

• 智能交通系统与信息技术 • 上一篇下一篇

事件干扰下的城市轨道交通客流诱导优化研究

赵明玺^1a,1b，马昌喜^1a,1b，麻存瑞^*2

1. 兰州交通大学，a.交通运输学院，b.高原铁路运输智慧管控铁路行业重点实验室，兰州730070； 2. 重庆邮电大学，现代邮政学院，重庆400065

收稿日期:2024-06-23 修回日期:2024-09-22 接受日期:2024-09-24 出版日期:2024-12-25 发布日期:2024-12-18
作者简介:赵明玺(1999- )，男，甘肃兰州人，博士生。
基金资助:
国家自然科学基金(52062027)；重庆市自然科学基金 (cstc2021jcyj-bshX0018)；重庆市教委人文社会科学研究项目 (20SKGH061)。

Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference

ZHAOMingxi^1a,1b, MAChangxi^1a,1b, MACunrui^*2

1a. School of Traffic and Transportation, 1b. Key Laboratory of Railway lndustry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. Modern Post School, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2024-06-23 Revised:2024-09-22 Accepted:2024-09-24 Online:2024-12-25 Published:2024-12-18
Supported by:
National Natural Science FoundationofChina (52062027)；Chongqing Natural Science Foundation Project of China (cstc2021jcyj-bshX0018)；Chongqing Municipal Education Commission of China (20SKGH061)。

摘要/Abstract

摘要： 城市轨道交通系统在早晚高峰、大型活动和恶劣天气等干扰事件下，通常出现运营中断或服务能力下降的情况。为有效缓解这些干扰事件对客流的负面影响，提高城市轨道交通系统的韧性，本文提出一种针对事件干扰的客流诱导优化方法。首先，在考虑事件干扰影响和乘客诱导服从率的基础上，以系统中乘客总出行时间最小化为目标，建立轨道交通客流诱导模型。其次，设计一种基于列生成的精确算法，运用Gurobi求解限制主问题，A*算法求解价格子问题及分支定界算法求解整数解。最后，通过实际案例分析发现，运用本文所设计的加速策略能够提升求解效率66%~89%，求解效率显著优于单独使用Gurobi；并通过模拟轻微干扰到严重干扰事件的情景表明，所提出的优化方法适用于不同规模的城市轨道交通客流，能够在多种强度的干扰事件中有效地诱导乘客出行路径。

关键词: 城市交通, 客流诱导, 列生成, 干扰事件, A*算法

Abstract: Urban rail transit systems often experience operational disruptions or reduced service capacity during peak hours, major events, and adverse weather conditions. To effectively mitigate the negative impacts of these disruptions on passenger flow and enhance the resilience of urban rail transit systems, this paper proposes an optimization method for passenger flow guidance in response to disruptive events. First, considering the impact of disruptive events and the compliance rate of passenger guidance, this paper develops a rail transit passenger flow guidance model with the goal of minimizing the total travel time of passengers in the system. Then, a column generation-based exact algorithm is designed, and Gurobi is used to solve the restricted master problem. The A* algorithm is applied to solve the pricing subproblem, and the branch-and-bound algorithm is utilized to find integer solutions. Through actual case analysis, it is found that the acceleration strategies designed in this paper can improve the solving efficiency by 66%~89%, with performance significantly superior to using Gurobi alone. Simulations of scenarios ranging from minor to severe disruptions demonstrate that the proposed optimization method is applicable to urban rail transit passenger flows of varying scales, effectively guiding passenger travel paths under various disruption intensities.

Key words: urban traffic, passenger flow induction, column generation, disruptive events, A* algorithm

中图分类号:

U121

赵明玺, 马昌喜, 麻存瑞. 事件干扰下的城市轨道交通客流诱导优化研究[J]. 交通运输系统工程与信息, 2024, 24(6): 76-85.

ZHAOMingxi, MAChangxi, MACunrui. Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 76-85.

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http://www.tseit.org.cn/CN/Y2024/V24/I6/76

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事件干扰下的城市轨道交通客流诱导优化研究

Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference

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