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.

中文翻译：

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FedAGAT：基于联邦社区和自适应图注意力网络的实时交通流预测

预测交通流量对于优化智能交通系统 (ITS) 和通过准确预测交通模式减少拥堵至关重要。然而，当前的集中式TFP系统存在局限性，例如训练速度慢、通信成本高和隐私问题。为了应对这些挑战，本研究提出了 FedAGAT，这是一种基于联邦社区和自适应时空图注意网络（AGAT）的短期交通流预测（TFP）系统。 FedAGAT 系统允许本地数据处理并仅与服务器共享模型更新。这可实现实时、可扩展且安全的 TFP——高效 ITS 的基本功能。 AGAT 用于捕获交通流中复杂的时空依赖性，而联邦学习则促进去中心化学习，增强隐私性。 FedAGAT 预测过程涉及四个步骤：使用谱社区检测划分本地子网络、基于全局参数进行本地训练、上传更新的参数以及基于聚合参数创建全局模型预测。为了评估 FedAGAT 的性能，利用两个真实世界的流量数据集对七个统计和深度学习模型进行基准测试。结果表明，FedAGAT 为短期和中期预测范围提供了相对较高的准确度。此外，FedAGAT 的预测与实际交通流量值非常匹配，整体性能与全局模型相当，同时需要的时间更少。