Data-driven intelligent system for emergency lane activation on expressways

This study proposes a comprehensive data processing framework designed to automate real-time emergency lane activation. The framework integrates several advanced technologies, including You Only Look Once v5 for vehicle detection, deep simple online and real-time tacking for vehicle tracking, and long short-term memory networks for traffic flow prediction. Additionally, the framework uses density-based spatial clustering of applications with noise and K-means clustering to classify traffic states into three categories: ‘smooth’, ‘stable’ and ‘congested’. These classifications are then used to determine the optimal time for activating emergency lanes, thereby alleviating congestion and improving overall traffic flow. The effectiveness of the proposed framework is validated through Monte Carlo simulations, which test its performance under a variety of traffic conditions. Experimental results demonstrate that the model achieves a 97% accuracy in classifying traffic states and results in a 22% improvement in traffic flow. This research introduces a novel, data-driven approach to intelligent traffic management, offering a reliable, cost-effective and automated solution for emergency lane activation, with significant potential to enhance real-time traffic management on expressways.