ML Driven Priority-Based Constrained Routing for Pollution Management in ITS

Intelligent Transportation Systems (ITS) seek to enhance traffic safety, mobility, and efficiency through advanced technologies. Priority assignment, a fundamental component of ITS, allocates priority to various types of vehicles or roads based on their significance and urgency. In this study, we explore the implementation of priority-based constrained routing for road vehicles in ITS, leveraging machine learning techniques and dynamic routing algorithms. Our objective is to enhance traffic management and efficiency by addressing the limitations of traditional routing methods that do not account for vehicle priorities. By employing machine learning models within a priority-based routing system, dynamic priorities can be accurately assigned to vehicles in real-time, facilitating more effective traffic management decisions. This approach mitigates congestion and reduces travel times for high-priority vehicles, presenting an innovative means of refining traffic control strategies. The findings hold substantial implications for real-world ITS systems, as they can alleviate traffic congestion, improve traffic flow, enhance efficiency, and manage pollution levels in specific areas.