广州城市轨道交通车站客流接驳交通方式划分改进模型
蔡涵哲1林俊彦2,3王治2,3叶霞飞2,3
Modes at Guangzhou Urban Rail Transit Stations
CAI Hanzhe1LIN Junyan2,3WANG Zhi2,3YE Xiafei2,3
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作者信息:1.广州地铁设计研究院股份有限公司，510010，广州
2.上海市轨道交通结构耐久与系统安全重点实验室，201804，上海
3.同济大学道路与交通工程教育部重点实验室，201804，上海
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Affiliation:1.Guangzhou Metro Design & Research Institute Co., Ltd., 510010, Guangzhou，China
2.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, 201804，Shanghai, China
3.The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 201804，Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.01.037
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中图分类号/CLCN:U293.1+3∶U231.4
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栏目/Col:研究报告
摘要:
［目的］为准确测算广州城市轨道交通车站接驳设施所需规模，需对车站各接驳交通方式下客流的分担率进行研究并预测。［方法］基于对广州城市轨道交通南村万博站、同和站等在不同天气状况下的进站客流接驳数据的实地调查，在传统MNL(多项Logit)模型的基础上，考虑天气、进出站接驳特征差异的影响，构建基于MNL模型的城市轨道交通车站客流接驳交通方式划分改进模型，并利用问卷调查的数据进行模型标定。［结果及结论］模型标定结果表明，仅接驳距离特征变量通过显著性检验，性别、出行目的等与轨道交通接驳方式选择间不存在明显的相关性；所研究的调查数据未捕捉到出行者年龄和接驳交通方式选择的相关性；对同和站晚高峰进站客流的晴天和雨天接驳交通方式划分改进模型进行检验，准确率分别达到86.0%和77.2%，说明考虑天气因素的城市轨道交通车站客流接驳交通方式划分改进模型是优于传统模型的。将城市轨道交通车站客流接驳交通方式划分改进模型应用于用地属性相似的目标站点，证实了该模型在实际客流预测中的有效性与合理性。
Abstracts:
［Objective］ In order to accurately calculate the required scale of the connection facilities at Guangzhou urban rail transit stations, it is necessary to study and predict the sharing rate of passenger flow under each transportation connection mode at the stations. ［Method］ Based on the on-site investigations of the inbound passenger flow connection data of Nancun Wanbo Station, Tonghe Station and other stations in Guangzhou urban rail transit under different weather conditions, on the basis of the traditional MNL (Multinomial Logit) model, and in consideration of the impact of weather and differences in the inbound and outbound connection characteristics, an improved model for classifying the passenger flow transportation connection modes at urban rail transit stations based on the MNL model is constructed, and calibrated by using the data from the questionnaire surveys. ［Result & Conclusion］ The results of model calibration indicate that only the characteristic variable of connection distance passes the significance test, and there is no obvious correlation between factors such as gender, travel purpose and the choice of rail transit connection modes. The investigated survey data fails to capture the correlation between the age of travelers and the choice of transportation connection modes. In the test of the improved model for classifying the transportation connection modes of the inbound passenger flow at Tonghe Station, on both sunny and rainy days， the passenger flow accuracy rates during the evening peak hours reach 86.0% and 77.2% respectively, showing that the improved model for the above scenario is superior to the traditional one. The improved model is applied to the target stations with similar land use attributes, confirming its effectiveness and rationality in actual passenger flow prediction.
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