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An effective spatiotemporal deep learning framework model for short-term passenger flow prediction.

Authors :
Wang, Xueqin
Xu, Xinyue
Wu, Yuankai
Liu, Jun
Source :
Soft Computing - A Fusion of Foundations, Methodologies & Applications; Jun2022, Vol. 26 Issue 12, p5523-5538, 16p
Publication Year :
2022

Abstract

The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14327643
Volume :
26
Issue :
12
Database :
Complementary Index
Journal :
Soft Computing - A Fusion of Foundations, Methodologies & Applications
Publication Type :
Academic Journal
Accession number :
156972602
Full Text :
https://doi.org/10.1007/s00500-022-07025-8