基于灵活编组策略的列车时刻表与车厢分配协同优化研究

doi:10.16097/j.cnki.1009-6744.2026.02.021

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 221-231.DOI: 10.16097/j.cnki.1009-6744.2026.02.021

基于灵活编组策略的列车时刻表与车厢分配协同优化研究

王鑫伟，许琰，孙立山^*

北京工业大学，城市交通学院，北京100124

收稿日期:2025-11-02 修回日期:2025-12-29 接受日期:2026-02-10 出版日期:2026-04-25 发布日期:2026-04-20
作者简介:王鑫伟（1997—），男，山东青岛人，博士生。
基金资助:
国家自然科学基金(52472317)；北京市自然科学基金(L231023)。

Collaborative Optimization of Train Timetabling and Carriage Allocation Based on Flexible Train Formation Strategy

WANG Xinwei, XU Yan, SUN Lishan^*

College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

Received:2025-11-02 Revised:2025-12-29 Accepted:2026-02-10 Online:2026-04-25 Published:2026-04-20
Supported by:
National Natural Science Foundation of China (52472317)；Natural Science Foundation of Beijing, China (L231023)。

摘要/Abstract

摘要： 为应对大城市地铁线路高峰时段客流时空分布不均衡引发的过饱和、乘客滞留与站台拥挤风险，本文提出一种新的列车运营策略：根据乘客出行距离（长途/短途）灵活分配并耦合车厢，在列车运行至客流较小区段时决策是否解耦。该策略将列车划分为前后两部车厢，并依据时变客流确定各部分车厢数量，实现列车运能与需求的精准匹配。同时，通过在上游站点协同控制长途乘客上车量，为下游短途乘客预留运能，提高大客流期间上下车客流交换率与列车实际载客效能，在全线范围内均衡滞留乘客并缓解繁忙车站拥挤。以最小化乘客总感知等待时间与车辆走行公里数为目标，构建灵活编组和车厢分配策略的列车时刻表与客流控制协同优化模型，并采用优化求解器Gurobi求解。结果表明：相较于固定编组且不进行车厢分配与客流控制的常规方案，本文策略下的优化方案可使总目标值减少6.93%，其中，车辆走行公里数降低9.38%；同时，优化方案能够更有效地均衡各站滞留乘客，降低聚集风险并提高列车平均满载率。进一步的参数灵敏度分析验证了滞留乘客感知等待时间系数对模型结果的显著影响，反映了控制滞留风险与节约运营成本之间的权衡。

关键词: 城市交通, 车厢分配, 协同优化, 列车时刻表, 灵活编组, 大客流场景

Abstract: To mitigate the peak-hour oversaturation, passenger accumulation, and platform crowding risks arising from spatiotemporally imbalanced demand on urban metro lines, this study proposes a novel distance-based and flexibly coupled train operation strategy. Carriages are flexibly allocated and coupled based on passenger travel distances (long-distance vs. short distance), and a decision on whether to decouple is made when trains enter low-demand sections. This strategy partitions the train into front and rear sets, whose sizes are dynamically determined under the time-varying passenger flow to achieve the precise matching between capacity and demand. In addition, a line-level coordinated boarding control is implemented by regulating long distance boarding at upstream stations to reserve the capacity for downstream short-distance riders. Thereby it improves the passenger exchange rate and effective carrying performance during peak periods, and balance the line-wide of stranded passengers while alleviating congestion at busy stations. With the objective of minimizing total perceived passenger waiting time and vehicle kilometers, a collaborative optimization model for the train timetable and passenger flow control is developed through incorporating flexible marshalling and carriage allocation strategies, and solved using Gurobi solver. Numerical experiments and sensitivity analyses indicate that, relative to the conventional fixed-formation scheme without car allocation or boarding control, the proposed approach improves the overall objective by 6.93%, including a 9.38% reduction in vehicle-kilometers. While it can more effectively balance the distribution of passenger accumulation across stations, and reduce the risk of gathering and get a higher average load factor. Further sensitivity analysis confirms the pronounced impact of the perceived waiting-time coefficient of stranded passengers on model outcomes, highlighting the trade-off between controlling the risks of detention and saving operating costs.

Key words: urban transportation, carriage allocation, collaborative optimization, train timetable, flexible train formation, large passenger flow scenario

中图分类号:

U121

王鑫伟, 许琰, 孙立山. 基于灵活编组策略的列车时刻表与车厢分配协同优化研究[J]. 交通运输系统工程与信息, 2026, 26(2): 221-231.

WANG Xinwei, XU Yan, SUN Lishan. Collaborative Optimization of Train Timetabling and Carriage Allocation Based on Flexible Train Formation Strategy[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 221-231.

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http://www.tseit.org.cn/CN/Y2026/V26/I2/221

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基于灵活编组策略的列车时刻表与车厢分配协同优化研究

Collaborative Optimization of Train Timetabling and Carriage Allocation Based on Flexible Train Formation Strategy

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