BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies

Predicting traffic flow effectively alleviates congestion. However, traditional methods tend to rely solely on historical traffic flow data, overlooking the correlation between multimodal traffic data, such as speed and occupancy collected by sensors placed on the road. This limitation results in low tolerance for abnormal situations. Moreover, the decentralization of multimodal data on edge devices may pose data anomalies or partial modal missing due to equipment damage or absence. To address these challenges, we propose a Block-Term tensor decomposition-based multimodal data feature fusion algorithm for traffic prediction. This approach enhances the accuracy and robustness of traffic flow prediction by considering correlations between various modal data, such as speed and occupancy rate. In response to the issues of scattered multimodal data anomalies and missing data on edge devices, and to ensure address privacy and security issues, we employ federated learning methods to achieve adaptive extraction and fusion of multi-modal data at the edges. Our method is tested on a real highway dataset, demonstrating superior prediction performance and robustness compared to traditional methods, particularly in the context of data anomalies or missing modalities.