Short-Term Passenger Flow Prediction Method for Urban Rail Transit Considering Station Classification

被引：0
作者：
Wang, Taizhou [1 ]
Xu, Jinhua [1 ]
Chen, Jianghui [1 ]
Li, Yan [1 ]
Ren, Lu [2 ]
机构：
[1] College of Transportation Engineering, Chang’an University, Xi’an
[2] Chang’an University Journal Center, Xi’an
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 19期
关键词：
complete ensemble empirical mode decomposition with adaptive noise; long short-term memory network; short-term prediction; station classification; Transformer; urban rail transit;
D O I：
10.3778/j.issn.1002-8331.2306-0338
中图分类号：
学科分类号：
摘要：
Accurate and reliable short-term forecasting of passenger flows can ensure the operation of urban rail transport. Considering the differences in the timing characteristics of passenger flows at different stations, a deep learning method for predicting passenger flows at urban stations is developed based on station classification. Firstly, stations are classified by dynamic time warping and the K-means algorithm, and the timing characteristics of the passenger flow of various stations are analyzed. Secondly, the complete ensemble empirical mode decomposition with adaptive noise is used to decompose passenger flow data of various stations to reduce the effects of data noise. Finally, a deep learning prediction method integrating long short-term memory and Transformer model is proposed to predict the passenger flow of different types of stations. The method is verified by using the passenger flow data of Xi’an Metro. The results show that the stations can be classified into four types according to the timing characteristics of passenger flow data on working days and non-working days: occupation-residential balance type, business office type, leisure and entertainment type, and dense residential type. Compared with the other three single models and three combined models, mean absolute error of passenger flow prediction results of the proposed method in different types of stations is reduced by 16.36%~51.02%, root mean square error is reduced by 10.35% ~50.76%, and mean absolute percentage error is reduced by 14.71% ~ 48.62%. Compared with the other six models, the prediction results of the station passenger flow data based on the statistics of different time intervals of 15 min, 30 min, 45 min and 60 min, the three indicators are respectively reduced by 12.63%~51.02%, 8.08%~49.12% and 6.83%~47.26%. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
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页码：343 / 353
页数：10
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