A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction

Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang (), Haifeng Song () and Lunming Qin
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Jinlong Li: Beijing Urban Construction Design and Development Group Co., Ltd., Beijing 100034, China
Haoran Chen: State Key Laboratory of Advanced Rail Autonomous Operation, School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Qiuzi Lu: Beijing Urban Construction Design and Development Group Co., Ltd., Beijing 100034, China
Xi Wang: State Key Laboratory of Advanced Rail Autonomous Operation, School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Haifeng Song: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Lunming Qin: College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China

Mathematics, 2025, vol. 13, issue 14, 1-18

Abstract: Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting.

Keywords: data-driven; passenger flow forecasting; dual attention mechanism; spatiotemporal convolutional (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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