大型活动散场期间城市轨道交通大客流时空分布预测及其应用
周峰1王文昱1赵周1文旭光2
Prediction and Application of Spatiotemporal Distribution of Large Passenger Flow in Urban Rail Transit During Crowd Dispersal of Large Scale Events
ZHOU Feng1WANG Wenyu1ZHAO Zhou1WEN Xuguang2
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作者信息:1.同济大学道路与交通工程教育部重点实验室， 201804， 上海
2.广西中国-东盟综合交通国际联合重点实验室， 530299， 南宁
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Affiliation:1.The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 201804, Shanghai, China
2.Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation, Nanning University, 530299, Nanning, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.05.005
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中图分类号/CLCN:U293.13
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栏目/Col:规划与投融资
摘要:
［目的］随着市民对文娱生活的多样化需求日益渐增，各城市举办的大型活动数量不断增加，大型活动诱发的大客流会对城市轨道交通常态运营造成干扰，存在安全风险，因此需从历史数据中挖掘客流特征，并提出客流预测方法，为轨道交通运营部门在大型活动期间的行车计划提供科学的决策支持。［方法］从多源影响因素的挖掘与分析出发，收集了起始车站、馆站距离、日期类型、天气类型、活动类型、活动等级、售票规模、出站人数等信息作为特征变量，考虑到随着时间推移可获得的自变量信息数量和精确度越来越高，设计了滚动数据驱动的XGBoost预测模型；基于乘客的出行轨迹，提出了大型活动散场大客流时空分布分析方法。以某城市车站附近场馆举办大型活动为例，验证所提预测方法的准确性。［结果及结论］所提客流预测方法在OD（起讫点）层面、线路层面、进站量层面均取得了较好的预测精度，能够为车站客流管控和网络运营调整提供决策依据。
Abstracts:
［Objective］With the citizens′ daily increasing demand for the diversification of cultural and recreational activities, the number of large-scale events held in cities is continuously increasing. The large passenger flows induced by these events can disrupt the normal operation of urban rail transit and pose safety risks. Therefore, it is necessary to extract passenger flow characteristics from historical data and propose passenger flow prediction methods to provide scientific decision-making support for rail transit operation authorities during large-scale events. ［Method］Starting from the exploration and analysis of multi-source influencing factors, information such as origin stations, venues-station distances, date types, weather types, event types, event scale, ticket sales volume, and exit passenger numbers is collected as feature variables. Considering that the amount of independent variable information available is increasing and its accuracy improves over time, a rolling data-driven XGBoost prediction model is designed. Based on passenger travel trajectories, a spatiotemporal distribution analysis method for large passenger flows during the crowd dispersal of large-scale events is proposed. The accuracy of the proposed prediction method is verified using the case of a large-scale event held at a venue close to station in a certain city. ［Result & Conclusion］The proposed passenger flow prediction method achieves high prediction accuracy at the OD (origin-destination) level, line level, and entry volume level. It can provide a decision-making basis for station passenger flow control and network operation adjustments.
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