大客流场景下列车时刻表与列车容量分配协同优化研究

doi:10.16097/j.cnki.1009-6744.2024.02.020

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (2): 199-207.DOI: 10.16097/j.cnki.1009-6744.2024.02.020

大客流场景下列车时刻表与列车容量分配协同优化研究

龚聪聪¹ ，杨立兴^*1 ，石俊刚² ，戚建国¹ ，周厚盛¹

1. 北京交通大学，系统科学学院，北京100044；2.华东交通大学，交通运输工程学院，南昌330013

收稿日期:2023-11-27 修回日期:2024-01-03 接受日期:2024-01-18 出版日期:2024-04-25 发布日期:2024-04-25
作者简介:龚聪聪(1995- )，女，河北邯郸人，博士生。
基金资助:
国家自然科学基金 (72361012, 72371015)。

Collaborative Optimization of Train Timetabling and Train Capacity Allocation for Large Passenger Flow Scenario

GONGCongcong¹,YANG Lixing^*1,SHI Jungang²,QI Jianguo¹,ZHOU Housheng¹

1. School of Systems Science, Beijing Jiaotong University, Beijing 100044, China; 2. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China

Received:2023-11-27 Revised:2024-01-03 Accepted:2024-01-18 Online:2024-04-25 Published:2024-04-25
Supported by:
NationalNaturalScienceFoundation of China (72361012, 72371015)。

摘要/Abstract

摘要： 为缓解城市轨道交通通勤线路中大客流车站及其下游车站的站台拥挤，降低候车乘客的聚集程度和安全风险，本文从时间和空间角度系统优化城市轨道交通列车容量资源配置。通过考虑时变客流需求和预留车厢策略，提出城轨列车时刻表与列车容量分配协同优化方法。具体地，通过引入列车发车时间、预留车厢数量和客流分配方案相关决策变量，以最小化预留车厢运营成本和站台乘客最大聚集数量为目标，建立列车时刻表与列车容量分配协同优化线性整数规划模型。其中，采用大M方法构建的客流分配约束能够满足乘客在时间和空间上的先到先服务原则。为验证所构建模型的有效性，设置4组数值算例进行对比实验，并采用优化求解器Gurobi求解。结果表明，相较于计划列车时刻表方案和两种单一优化策略方案，协同优化方案能够显著降低最大乘客聚集数量分别约为60%、52%和31%，降低乘客总等待时间分别约为29%、17%和29%。即协同优化方法能够均衡城轨列车容量的时空分布，从而有效降低站台候车乘客的聚集风险。

关键词: 城市交通, 列车容量时空分配, 协同优化, 列车时刻表, 大客流场景, 站台拥挤

Abstract: To alleviate platform congestion at large passenger flow stations and their downstream stations on urban rail transit commuter lines, and reduce the crowding level and safety risks, this paper systematically optimizes the allocation of train capacity resources from temporal and spatial levels. By considering time-varying passenger demand and the train carriage reservation strategy, a collaborative optimization method for train timetabling and train capacity allocation problems is proposed. Specifically, decision variables related to train departure time, number of reserved train carriages, and passenger assignment plan are introduced and an integer linear programming model for the train timetabling and train capacity allocation problem is formulated, to minimize the operational cost of train carriage reservation and the maximum number of waiting passengers on platforms. Among them, the passenger assignment constraints formulated by the Big-M method obey the first-in-first-out principle in time and space levels. To validate the effectiveness of the constructed model, four sets of numerical experiments are implemented by the Gurobi solver directly. The results show that, compared to planned train timetable and two single optimization strategies, the collaborative optimization method can significantly reduce the maximum number of waiting passengers by approximately 60%, 52%, and 31%, and reduce the total passenger waiting time by about 29%, 17%, and 29%, respectively. That is, the collaborative optimization method can balance the temporal and spatial distribution of the urban rail train capacity and effectively mitigate the risk of passenger crowding at platforms.

Key words: urban traffic, space-time allocation of train capacity, collaborative optimization, train timetable, large passenger flow scenario, platform congestion

中图分类号:

U121

龚聪聪, 杨立兴, 石俊刚, 戚建国, 周厚盛. 大客流场景下列车时刻表与列车容量分配协同优化研究[J]. 交通运输系统工程与信息, 2024, 24(2): 199-207.

GONGCongcong, YANG Lixing, SHI Jungang, QI Jianguo, ZHOU Housheng. Collaborative Optimization of Train Timetabling and Train Capacity Allocation for Large Passenger Flow Scenario[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(2): 199-207.

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http://www.tseit.org.cn/CN/Y2024/V24/I2/199

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大客流场景下列车时刻表与列车容量分配协同优化研究

Collaborative Optimization of Train Timetabling and Train Capacity Allocation for Large Passenger Flow Scenario

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