Federated Learning Approach for Multi-Regional Traffic Flow Prediction

Accurate traffic flow prediction is a core task in intelligent transportation systems because urban traffic observations are spatially distributed, temporally dynamic, and commonly held by different regional management entities. Existing centralized and local models remain limited when traffic data are non-independent and identically distributed across regions, and when raw data cannot be directly exchanged because of privacy, ownership, and communication constraints. To address these challenges, this study proposes a personalized similarity-aware federated spatiotemporal learning framework for multi-regional traffic flow prediction. The framework integrates three mechanisms: client-specific adaptation for regional distributional heterogeneity, adaptive delayed graph learning for dynamic congestion propagation, and similarity-aware federated aggregation for information-quality-based cross-client collaboration. Spatial dependency, temporal evolution, traffic-flow-theory-informed variables, road attributes, and temporal contextual features are jointly modeled without sharing raw client data. Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed method consistently outperforms independent training, FedAvg, FedProx, FedSTN-inspired, and FedAGCN-inspired baselines. On the Q-Traffic grid-level setting, the proposed adaptive graph version reduces MSE by 35.3% compared with FedAvg, while the CNN version reduces MSE by 27.5%. Under the cluster-level setting, the adaptive graph version reduces MSE by 26.3% compared with FedAvg. Ablation, sensitivity, communication-cost, differential-privacy, and client-dropout analyses further show that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.