列车延误下城市轨道交通路网客流动态诱导研究

doi:10.16097/j.cnki.1009-6744.2026.02.018

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 192-202.DOI: 10.16097/j.cnki.1009-6744.2026.02.018

列车延误下城市轨道交通路网客流动态诱导研究

何洁^a，郭建媛^*a，王子瑜^a，秦勇^b，王若愚^b，贾利民^a

北京交通大学，a.交通运输学院；b.先进轨道交通自主运行全国重点实验室，北京100044

收稿日期:2025-09-25 修回日期:2025-12-25 接受日期:2025-12-29 出版日期:2026-04-25 发布日期:2026-04-20
作者简介:何洁（2000—），女，四川绵阳人，博士生。
基金资助:
北京市科技计划 (Z211100004121013)。

Dynamic Passenger Flow Induction in Urban Rail Transit Networks During Train Delays

HE Jie^a, GUO Jianyuan^*a, WANG Ziyu^a, QIN Yong^b, WANG Ruoyu^b, JIA Limin^a

a. School of Traffic and Transportation; b. State Key Laboratory of Advanced Rail Autonmous Operation, Beijing Jiaotong University, Beijing 100044, China

Received:2025-09-25 Revised:2025-12-25 Accepted:2025-12-29 Online:2026-04-25 Published:2026-04-20
Supported by:
Beijing Municipal Science and Technology Program, China (Z211100004121013)。

摘要/Abstract

摘要： 在大规模路网中，列车延误会造成大量乘客出行滞留拥堵，通过信息诱导进行客流疏解是必要的手段。但因为受影响乘客数量庞大且行程多样，如何通过有效诱导提升出行效率并缓解路网拥堵，是一个极具挑战性的问题。为此，本文构建以最小化乘客平均等待时间与路网客流分布非均衡度为目标的客流动态诱导模型。该模型在常规约束基础上，融合随机后悔最小化和随机效用最大化理论，建立诱导信息影响下的路径选择约束。针对所提出的高维变量复杂约束模型，设计一种多目标异步并行鲸鱼优化算法（NFM_WOA），并将模型与算法应用于北京市城市轨道交通路网的实际列车延误场景进行案例分析。结果表明，通过本文提出方法对受影响的77826对OD（Origin-Destination）进行分时段动态诱导，受影响乘客平均等待时间降低12.107%，路网客流分布非均衡度优化8.582%，高满载率区间数量占比下降39.19%，有效验证了所提方法在列车延误场景下的客流诱导效能；同时，本文提出的NFM_WOA算法相较传统鲸鱼优化算法求解耗时减少94.02%，显著提升其在列车延误场景下大规模变量求解的效率。

关键词: 城市交通, 客流诱导, 鲸鱼优化算法, 路径选择, 列车延误

Abstract: In large-scale networks, train delays can cause significant passenger congestion and travel disruptions. It is an essential measure to manage passenger flow through information induction. However, due to the large number of affected passengers and the diversity of their travel plans, it presents a highly challenging problem that how to effectively guide them to improve travel efficiency and alleviate congestion on the network. Accordingly, this paper proposes a induction model of dynamic passenger flow aiming to minimize both the average passenger waiting time and the imbalance of passenger flow distribution across the network. Based on conventional constraints, the model integrates the Random Regret Minimization and Random Utility Maximization theories to establish the route choice constraints under the influence of induction information. A multi-objective asynchronous parallel Whale Optimization Algorithm (NFM_WOA) is designed to propose a high-dimensional variable complex constraint model. The model and algorithm are applied to a case study of actual train delay scenarios in urban rail transit network of Beijing. The results indicate that the average passenger waiting time of affected travelers is reduced by 12.107%, and the imbalance of passenger flow distribution across the network is improved by 8.582%, and the proportion of high-load sections decreases by 39.19% by applying the proposed method to conduct time-dependent dynamic induction for 77 826 affected OD pairs. This validates the effectiveness of the proposed method in guiding passenger flow during train delays. Meanwhile, the proposed NFM_WOA algorithm reduces computation time by 94.02% compared to the traditional Whale Optimization Algorithm. It significantly enhances the efficiency in solving large-scale variable problems under train delay scenarios.

Key words: urban transportation, passenger flow induction, Whale Optimization Algorithm, route choice, train delay

中图分类号:

U121

何洁, 郭建媛, 王子瑜, 秦勇, 王若愚, 贾利民. 列车延误下城市轨道交通路网客流动态诱导研究[J]. 交通运输系统工程与信息, 2026, 26(2): 192-202.

HE Jie, GUO Jianyuan, WANG Ziyu, QIN Yong, WANG Ruoyu, JIA Limin. Dynamic Passenger Flow Induction in Urban Rail Transit Networks During Train Delays[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 192-202.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.02.018

http://www.tseit.org.cn/CN/Y2026/V26/I2/192

参考文献

[1]尹浩东.运营中断条件下城市轨道交通乘客出行行为建模与客流诱导优化研究[D].北京:北京交通大学, 2017. [YINHD.Modelingpassengertravelbehaviorand guidanceoptimization fordisruptionmanagement in the urban rail transit network[D]. Beijing: Beijing Jiaotong University,2017.]

[2]于丁原,姚恩建,刘莎莎,等.运营突发事件下城市轨道交通诱导信息发布策略研究[J].交通运输系统工程与信息,2023,23(5):227-237. [YUDY,YAOEJ,LIU SS, et al.Guidance informationreleasestrategyduring operationemergencies inurbanrail transit[J]. Journal of Transportation Systems Engineering and Information Technology,2023,23(5):227-237.]

[3]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. [ZHAO M X, MA C X, MA C R. Urban rail transit passenger flow induction optimization under event interference[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 76-85.]

[4]赵若愚.拥堵条件下城市轨道交通客流诱导方法与系统研究[D]. 北京: 北京交通大学, 2019. [ZHAO R Y. Research on urban rail transit passenger guidance method andsystem under congestion condition: Taking Guangzhou Metro as an example[D]. Beijing: Beijing Jiaotong University, 2019.]

[5]LIANG Y, ANTONIOU C, SADRANI M, et al. Joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling[J]. IEEE Transactions on Intelligent Transportation Systems, 2025.

[6]MO B C, KOUTSOPOULOS H N, SHEN Z J M, et al. Robust path recommendations during public transit disruptions under demand uncertainty[J]. Transportation Research Part B: Methodological, 2023, 169: 82-107.

[7]韩宝明,陈佳豪,周玮腾,等.城市轨道交通运营中断条件下乘客路径选择模型[J].同济大学学报(自然科学版), 2023, 51(2): 238-246. [HAN B M, CHEN J H, ZHOU W T, et al. Passenger route choice model under the condition of urban rail transit operation disruption[J]. Journal of Tongji University (Natural Science), 2023, 51 (2): 238-246.]

[8] 许心越,谢兰诗雨.诱导信息下城市轨道交通乘客路径选择行为研究[J].交通运输系统工程与信息,2022, 22(3): 63-73. [XU X Y, XIE L S Y. Route choice behavior of urban rail transit passengers based on guidance information[J]. Joumal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 63-73.]

[9] LENG N, CORMAN F. Communicating delays and adjusted disposition timetables: Modelling and evaluating the impact of incomplete information to passengers[J]. Expert Systems with Applications, 2022, 191: 116265.

[10] ZHUO S Y, ZHU X N, SHANG P, et al. Passenger route and departure time guidance under disruptions in over saturated urban rail transit networks[J]. Transportation Research Record, 2025, 2679(1): 2015-2034.

[11] YIN H D, WU J J, LIU Z Y, et al. Optimizing the release of passenger flow guidance information in urban rail transit network via agent-based simulation[J]. Applied Mathematical Modelling, 2019, 72: 337-355.

[12] 贾飞凡.城市轨道交通乘客路径动态诱导策略优化研究[D]. 北京: 北京交通大学, 2021. [JIA F F. Optimization of passenger route dynamic guidance strategy in urban rail transit network[D]. Beijing: Beijing Jiaotong University, 2021.]

[13] YONG P, MO Z Y, LIU S. Passenger's routes planning in stochastic common-lines' multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation[J]. Archives of Transport, 2021, 59(3): 73-92.

[14] ZHOU, Y Q, CHEN J F, ZHONG M H, et al. Evacuation effect analysis of guidance strategies on subway station based on modified cellular automata model[J]. Safety Science, 2023, 168: 106309.

[15] WANG Y, ZHOU Y, YANG H, et al. Integrated optimization of bus bridging service design and passenger assignment in response to urban rail transit disruptions[J]. Transportation Research Part C: Emerging Technologies, 2023, 150: 104098.

[16] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.

列车延误下城市轨道交通路网客流动态诱导研究

Dynamic Passenger Flow Induction in Urban Rail Transit Networks During Train Delays

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