Platform-Based Passenger Flow Prediction in Metro Systems: A Novel CNN-BILSTM-Attention Approach

Accurate platform-based passenger flow prediction based on deep learning technology becomes crucial for efficient operation and management; in particular, the prediction integrating external weather factors, temporal dependencies and spatial features is desired but has not been addressed. This paper is different from the previous station-based passenger flow prediction, but reconstructs data recorded by the Automatic Fare Collection System (AFCS) for platform-based prediction. Existing deep learning techniques often struggle with issues such as high computational cost in traffic flow prediction. To address these issues, a novel passenger flow prediction model is proposed that integrates convolutional neural networks (CNN) with bi-directional long short-term memory networks (BILSTM) and an attention mechanism (CNN-BILSTM-Attention). The proposed model takes preprocessed numerical weather features, temporal and spatial features as input. The CNN extracts spatial patterns from passenger flow data, the BILSTM captures temporal dependencies and the attention mechanism dynamically adjusts the importance weights of features at different time slots. By integrating these components, the model effectively captures spatiotemporal patterns while accounting for weather impacts. Experimental results demonstrate that the proposed approach outputs an efficient and accurate prediction.