Leveraging Edge Computing and Privacy-Enhanced Prediction Sharing for Urban Traffic Forecasting

In today’s urban transportation landscape, the accurate and timely prediction of traffic conditions holds immense significance for optimizing traffic flow and resource allocation. However, conventional centralized methods for traffic prediction come with the drawback of data collection from various roads, a process that can introduce latency and privacy concerns. To address these pressing challenges, our innovative approach seamlessly integrates road spatiotemporal data, edge computing, and privacy protection inspired by federated learning. We utilize Long Short-Term Memory (LSTM) networks to make real-time predictions of vehicle speeds on individual road segments. Furthermore, we introduce Graph Neural Networks (GNN) to effectively amalgamate predictive insights from the target road and its neighboring roads, effectively capturing intricate spatial relationships within traffic data. Our approach excels in reducing data requirements and alleviating computational burdens, enabling straightforward deployment on edge servers tailored to individual road segments. Most importantly, it prioritizes privacy by circumventing the exchange of raw data, instead opting for the selective sharing of predictions between segments. Our rigorous experimental results demonstrate that our approach outperforms traditional federated averaging algorithms, underscoring its remarkable security attributes while achieving superior predictive accuracy.