Prediction of Short-term Passenger Flow of High-speed Railway Integrated Passenger Hub under Station-city Integration

被引：0
|
作者：
Zhou L. [1 ,2 ]
Wang Y. [3 ]
Xie Y. [3 ]
Yang J. [3 ]
Gong W. [2 ]
机构：
[1] Postgraduate Department, China Aeademy of Railway Seienees, Beijing
[2] Transportation & Economies Research Institute, China Aeademy of Railway Seienees Corporation Limited, Beijing
[3] School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2023年 / 45卷 / 04期
关键词：
graph attention network; high-speed rail integrated hub; land use; short-term passenger flow prediction; station-city integration;
D O I：
10.3969/j.issn.1001-8360.2023.04.001
中图分类号：
学科分类号：
摘要：
Under the background of station-city integration, short-term passenger flow prediction is an important basis for the operation organization, coordinated management, and emergency response of the high-speed rail integrated hub. Based on the analysis of the interaction mechanism between the subway outbound passenger flow in the high-speed rail integrated hub, this paper studied the passenger flow topological relationship between the high-speed rail station, the surrounding land use, and the urban rail system. The land use characteristics were characterized by the number of various points of interest (POI) around the hub station, and the ridge regression method was used to calculate the passenger flow attracted by the land use around the hub station. Based on the interaction between the inbound passenger flow in the railway station, the passenger flow attracted by the land use around the hub, and the outbound passenger flow of the urban rail station, a passenger flow prediction model based on the Graph Attention Network (GAT) was proposed. On this basis, a multi-head attention ( MHA) mechanism was introduced to adaptively obtain the weight of railway inbound passenger flow and the passenger flow attracted by the land use under different time periods and different dates to accurately predict the passenger flow of the subway out of the high-speed rail integrated hub. In order to verify the validity of the model, an analysis was carried out with the Beijing South High-Speed Railway Integrated Hub as an example. The results show that the prediction accuracy of this model is significantly improved compared with other prediction models such as SVR, LSTM, and GCN. © 2023 Science Press. All rights reserved.
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页码：1 / 7
页数：6
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