Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
Passenger flow predictions are of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely the Spatial–Temporal Graph Sequence with Attention Network (STGSAN), was proposed to predict transit passenger flow. The algorithm mainly focused on the following three aspects: (1) a graph attention network (GAT) was used to capture the spatial correlation of various bus stops; (2) to make full use of the historical and real-time data, a bidirectional long short-term memory and attention mechanism was conducted to extract the temporal correlation of historical ridership at bus stations; and (3) external factors that affect passenger choices were taken into account. We conducted an experiment using field data collected in Urumqi, China. After comparison with five other models, the proposed model was proven to have excellent performance prediction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China, which provided support for the projects entitled “Connected vehicle big data driven expressway multi-objective coordinated control fusing deep learning and traffic flow model” (Award No. 71901070).
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 29, 2021
Accepted: Dec 8, 2021
Published online: Jan 28, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 28, 2022
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