The Prediction of Flow in Railway Station Based on RRC-STGCN

Abstract

Predicting passenger flow is crucial for effective management and safety in railway stations. Accurate prediction of passenger flow facilitates effective allocation of staff tasks, enhances the efficient utilization of waiting areas, ensures passenger safety, and promotes a smooth travel experience for passengers. However, accurately establishing the spatial-temporal relationship of passenger flow within the station and predicting the passenger flow in each region and time period is challenging due to the varying waiting habits and train preferences of each individual passenger. In this paper, we propose a Residual-RNN-Channel Spatial-Temporal Graph Convolutional Network (RRC-STGCN), which utilizes the channel attention mechanism and residual structure. The model divides the data with a time dimension into multiple periods, capturing spatial-temporal correlations through the channel attention mechanism, and extracting spatial-temporal dependencies from the feature maps using the spatial-temporal convolution module. The model uses a residual structure to fuse features in order to enhance the accuracy of prediction results. In addition, we conduct a comprehensive experimental evaluation using a real dataset of railway station passenger, demonstrating that the RRC-STGCN model outperforms five well-known baselines. Moreover, we provide visualizations of the prediction results, effectively showcasing the dynamic changes in passenger flow in each waiting area.

Keywords

• Spatial-temporal graph neural network
• deep learning
• passenger flow prediction
• railway station
• residual structure
• channel attention