Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System

Abstract

Accurate short-term metro passenger flow prediction is critical for urban transit management, yet existing methods face two key challenges: (1) privacy risks from centralized data collection and (2) limited capability to model spatiotemporal dependencies. To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). Unlike conventional approaches that require raw data aggregation, our method facilitates collaborative model training across metro stations while keeping data stored locally. The CNN is employed to extract spatial patterns, such as passenger correlations between adjacent stations, while the BIGRU captures bidirectional temporal dynamics, including peak-hour evolution. This architecture effectively eliminates the need for sensitive data sharing. We validate the framework using real-world datasets from Shenzhen Metro, and our key innovations include a privacy-preserving mechanism through federated parameter aggregation, joint spatial-temporal feature learning without the need for raw data transmission, and enhanced generalization across heterogeneous stations.