Publication

Abstract : Efficient management and recuperation of traffic disturbances is a significant factorin maintaining the reliability and performance of roadways in a metropolitan city. The proposed method leverages Particle Swarm Optimization (PSO) in comparison with Genetic Algorithm and Differential Evolution, to optimize the scheduling and planning of optimized routes for vehicles, thereby minimizing delays and disruptions in roadway systems. The application of PSO for road traffic management helps to model the roadway's traffic flow and disturbance scenarios, integrating them into a PSO-based framework that iteratively adjusts traffic signals and routes to achieve optimal recovery outcomes. The effectiveness of PSO is evaluated through a fitness function with additional parameters, which has been solved numerically, demonstrating significant optimum fitness values and average runtime compared to GA and DE. Integrating PSO for the complex and dynamic nature of road traffic disturbances will provide a robust solution for enhancing overall system efficiency and traveller satisfaction and experience.