Spatio-Temporal Self-Attention Network for Origin–Destination Matrix Prediction in Urban Rail Transit

Wenzhong Zhou (), Tao Tang and Chunhai Gao
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Wenzhong Zhou: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Tao Tang: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Chunhai Gao: Traffic Control Technology Co., Ltd., Beijing 100070, China

Sustainability, 2024, vol. 16, issue 6, 1-17

Abstract: Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving URT operation. However, due to the problems such as the unavailability of the OD matrix of the current day, high dimension and long-range spatio-temporal dependencies, it is difficult to further improve the prediction accuracy of an OD matrix. In this paper, a novel spatio-temporal self-attention network (SSNet) for OD matrix prediction in URT is proposed to further improve the prediction accuracy. In the proposed SSNet, a lightweight yet effective spatio-temporal self-attention module (STSM) is proposed to capture complex long-range spatio-temporal dependencies, thus helping improve the prediction accuracy of the proposed SSNet. Additionally, the finished OD matrices on previous days are used as the only data source without the passenger flow data on the current day in the proposed SSNet, which makes it possible to predict the OD matrices of all time intervals on the current day before the operation of the current day. It is demonstrated by experiments that the proposed SSNet outperforms three advanced deep learning methods for short-term OD prediction in URT, and the proposed STSM plays an important role in improving the prediction accuracy.

Keywords: origin–destination prediction; deep learning; self-attention mechanism; urban rail transit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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