Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facilitating privacy-preserving and decentralized real-time traffic flow prediction.

翻译：精准的实时交通流预测可有效缓解交通拥堵及其负面影响。现有集中式深度学习方法虽展现出较高预测精度，但因交通数据的敏感性而面临隐私保护问题。此外，新兴的分布式学习方法（包括联邦学习）在交通预测领域的应用主要聚焦于离线学习。本文提出BFRT，一种基于区块链的联邦学习架构，利用实时数据与边缘计算实现在线交通流预测。该方法在保障底层数据隐私的同时，能在车联网边缘实现实时去中心化模型训练。我们融合GRU与LSTM模型，并利用动态采集的主干道交通数据分片开展大量实验。在Hyperledger Fabric上构建了所提出的许可型区块链网络原型，并通过虚拟机模拟边缘节点进行广泛测试。实验结果表明，该方法优于集中式模型，凸显了其在实现隐私保护与去中心化实时交通流预测方面的可行性。