Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies

Abstract

The smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. This approach leverages the sophisticated functionalities of TCNs to evaluate and estimate traffic trends, such as congested phases, traffic flow, and ideal mobility routes. The system guarantees data privacy and utilizes decentralized information analysis using Federated Learning. In this approach, every point in the intelligent city network, including traffic sensors and cameras, serves to collectively comprehend traffic patterns without disclosing raw data. Using this cooperative method not only improves the model’s ability to forecast outcomes accurately but also enables efficient real-time traffic management, with the ability to adapt to changing conditions. The method has shown significant efficacy in enhancing traffic flow and mitigating congestion. The critical criteria are a 20% drop in average commuting times, a 25% drop in traffic congestion, and a 15% enhancement in emergency response times. These statistics highlight the system’s efficiency in improving urban traffic control by integrating modern technologies.

Keywords

• Decentralized learning
• mobile patterns
• transport
• traffic prediction
• accuracy
• normalization