Novel Hybrid Spatiotemporal Convolution Neural Network Model for Short-Term Passenger Flow Prediction in a Large-Scale Metro System

Accurate and reliable prediction of subway passenger flow is a particularly challenging application of spatiotemporal forecasting, due to the time-varying travel patterns and the complex spatial dependencies on subway networks. To address these challenges, this study proposes a novel spatiotemporal graph convolutional bidirectional long short-term memory neural network model combined with an attention mechanism (At-STGCN-BiLSTM) to better predict short-term passenger flow for all stations in a large-scale metro system. The STGCN-BiLSTM aims to capture the attributes of spatiotemporal characteristics of subway stations, and the attention mechanism helps account for the correlation between historical data and current moment inbound passenger flow. The performance of the short-time passenger flow forecast model is analyzed by different time intervals. Experimental results show that the proposed model outperforms baseline models on Wuhan, China, subway data. The value of root-mean square error (RMSE) and mean absolute error (MAE) decreased by 7.33% and 9.38%, respectively, compared with the baseline models at the 15-min interval. The attention mechanism in the proposed model can effectively improve the prediction capability of peak and nonperiodic passenger flow variations. The research not only is of great help to the passenger flow organization and emergency management of the subway, but also plays a vital role in the work of rail transit regulation, rail transit alarm release, and service efficiency improvement.

• Record URL:
  • https://doi.org/10.1061/JTEPBS.TEENG-7997
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Authors:
  • Li, Zhihong
  • Wang, Xiaoyu
  • Cai, Hua
  • Xu, Han
• Publication Date: 2024-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04024016
• Serial:
  • Journal of Transportation Engineering, Part A: Systems
  • Volume: 150
  • Issue Number: 5
  • Publisher: American Society of Civil Engineers
  • ISSN: 2473-2907
  • EISSN: 2473-2893
  • Serial URL: http://ascelibrary.org/journal/jtepbs

Subject/Index Terms

• TRT Terms: Forecasting; Neural networks; Passenger traffic; Peak hour traffic; Subways
• Identifier Terms: Wuhan Metro (China)
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Railroads;

Filing Info

• Accession Number: 01913430
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Mar 29 2024 4:58PM