A Federated Spatio-Temporal Learning Framework for Cross-Modal Transport Flow Predictions

Transportation systems are shaped by multiple transport modes and their interactions. Planning and management increasingly rely on understanding these interplays; accordingly, cross-modal predictions offer the potential to improve forecasts for individual modes of transport. However, data privacy concerns hinder data sharing across providers. This paper presents a Federated Learning framework for cross-modal transport flow and demand prediction, enabling multiple transport organizations to collaboratively enhance prediction accuracy while preserving data privacy, especially spatial information. Our approach integrates data across transport modes and addresses data heterogeneity and sparse sensor coverage. Evaluations on two real-world cross-modal datasets show that the proposed model improves prediction accuracy, especially for transport modes with fewer data. The framework adapts to varying transport network sizes and generalizes well across heterogeneous environments, demonstrating the practical value of privacy-preserving collaboration for building resilient and adaptive mobility systems.