都市快轨交通

行车延误下线网短时客流类比推测方法及应用

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张月坤 ¹ , 刘军 ¹ , 王月玥 ² , 叶明玉 ³

都市快轨交通 | 学术探讨 2025,38(6): 33-39

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都市快轨交通 |学术探讨 2025 , 38 (6) : 33 -39

行车延误下线网短时客流类比推测方法及应用

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张月坤¹, 刘军¹, 王月玥², 叶明玉³

作者信息

^1.北京交通大学交通运输学院，北京　100044

^2.北京轨道交通路网管理有限公司，北京　100101

^3.北京京投卓越科技发展有限公司，北京　100029

通讯作者:

刘军，男，博士，教授，主要从事轨道交通运输理论与方法研究，jliu@bjtu.edu.cn

作者简介:

张月坤，男，博士研究生，高级工程师，从事轨道交通路网辅助决策系统理论与方法研究，yuekun_zhang@126.com

An Analogy-based Deducing Method of Short-Term Passenger Flow in Urban Rail Transit Delays

Yuekun ZHANG¹, Jun LIU¹, Yueyue WANG², Mingyu YE³

Affiliations

^1.School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044

^2.Beijing Metro Network Administration Co., Ltd., Beijing 100101

^3.Beijing BII Technology Development Co., Ltd., Beijing 100029

出版时间: 2025-12-10 doi: 10.3969/j.issn.1672-6073.2025.06.005

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摘要

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针对超大城轨交通网络中列车延误对动态客流影响的问题，以常态下客流状况为基础，结合数据驱动和模型驱动方法的优势，提出一种城轨行车延误下受影响乘客范围和数量的短时推测方法。首先，基于常态下乘客出行链OD路径明细数据，通过分析延误区段乘客流向，界定受影响乘客范围并构建客流数量推测模型。其次，针对受影响乘客群体，依据延误情况下的乘客出行选择行为，标定乘客阻抗敏感度系数，预测乘客组分（等待、绕行、损失）的结构变化。最后，选取北京城轨线网多个实际行车延误场景进行验证。结果表明：该方法适用于北京城轨线网多个场景，在工作日早高峰时段推测的平均相对误差为4.8%，以5 min颗粒度预测未来30 min内客流各项指标的耗时不超过140 s，具有良好的普适性、精准性和时效性。目前，该方案已在北京市轨道交通指挥中心成功应用，为线网调度员提供客流风险预警、乘客诱导和应急协调支持。

关键词

城市轨道交通 / 客流预测 / 行车延误 / 客流组分 / 应急处置

Abstract

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In the operation of ultra-large-scale urban rail transit networks, train operation delays occur frequently. Accurately and efficiently predicting the number and scope of affected passengers is crucial for network command and dispatch. Based on the normal passenger flow situation, this paper combines the advantages of data-driven and model-driven methods and proposes a short-term prediction method for the scope and number of affected passengers under urban rail train delays. First, based on the detailed OD path data of passenger travel chains under normal conditions, by analyzing the passenger flow direction in the delayed section, the scope of affected passengers is defined and a passenger flow quantity prediction model is constructed. Second, for the affected passenger group, according to the travel choice behavior of passengers under delay conditions, the passenger impedance sensitivity coefficient is calibrated to predict the structural changes of passenger components (waiting, detouring, and loss). Finally, multiple actual train delay scenarios in the Beijing urban rail network are selected for verification. The results show that this method can be effectively applied to multiple scenarios in the Beijing urban rail network. The average relative error of the prediction during the morning peak on weekdays is 4.8%. The time-consuming for predicting various passenger flow indicators within the next 30 minutes with a 5 minute granularity does not exceed 140 seconds, which shows good universality, accuracy, and timeliness. Currently, this solution has been successfully applied in the Beijing Rail Transit Command Center, providing passenger flow risk warnings, passenger guidance, and emergency coordination support for network dispatchers.

Key words

urban rail transit / passenger flow prediction / urban rail transit delays / passenger components / emergency response

引用本文

张月坤, 刘军, 王月玥, 叶明玉. 行车延误下线网短时客流类比推测方法及应用. 都市快轨交通, 2025 , 38 (6) : 33 -39 . DOI: 10.3969/j.issn.1672-6073.2025.06.005

Yuekun ZHANG, Jun LIU, Yueyue WANG, Mingyu YE. An Analogy-based Deducing Method of Short-Term Passenger Flow in Urban Rail Transit Delays[J]. Urban Rapid Rail Transit, 2025 , 38 (6) : 33 -39 . DOI: 10.3969/j.issn.1672-6073.2025.06.005

基金

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北京市发展和改革委员会建设项目(京发改审2017134)

参考文献

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2025年第38卷第6期

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文章信息

doi: 10.3969/j.issn.1672-6073.2025.06.005

接收时间：2025-03-26
首发时间：2026-10-19
出版时间：2025-12-10

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出版历史

收稿日期：2025-03-26
修回日期：2025-06-08

基金

北京市发展和改革委员会建设项目(京发改审2017134)

作者信息

^1.北京交通大学交通运输学院，北京　100044

^2.北京轨道交通路网管理有限公司，北京　100101

^3.北京京投卓越科技发展有限公司，北京　100029

通讯作者:

刘军，男，博士，教授，主要从事轨道交通运输理论与方法研究，jliu@bjtu.edu.cn

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https://castjournals.cast.org.cn/joweb/dskgjt/CN/10.3969/j.issn.1672-6073.2025.06.005

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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