FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

• Published in: Information sciences 2024-05, Vol.667, p.120482, Article 120482
• Main Authors: Al-Huthaifi, Rasha, Li, Tianrui, Al-Huda, Zaid, Li, Chongshou
• Format: Article
• Language: English
• Subjects: Adaptive graph attention networks; Community detection; Federated learning; Horizontal local road network; Intelligent transportation systems; Traffic flow prediction
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120482

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time. •Introducing FedAGAT: A model balancing accuracy, speed, and privacy in traffic flow prediction.•Improved spectral community detection boosts federated learning in global-local road networks.•Enhanced federated learning scalability with FedAVG and random sub-sampling for traffic flow.•FedAGAT outperforms 7 models in traffic prediction using METR-LA, PEMS-BAY with lower costs.•FedAGAT shows superior training and inference times against DCRNN, GMAN, ASTGAT baselines.