Optimized Traffic Routing System for Urban Congestion Management

Traffic congestion is a major challenge in modern urban areas, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic control systems often rely on fixed signal timing, which lacks adaptability to dynamic traffic conditions. To overcome these limitations, the study proposes an Optimized Traffic Routing System for Urban Congestion Management that integrates multiple algorithms, including Fixed Cycle, Longest Queue First, Q-learning, and Search-Based Techniques which combines Genetic Algorithms and A Search* where Genetic Algorithm optimizes traffic signal timing through evolutionary methods, while A* search dynamically reroutes vehicles to minimize congestion by finding the shortest and least crowded paths. The approach utilizes reinforcement learning, heuristic optimization, and real-time simulations to dynamically optimize traffic signals and improve vehicle throughput while reducing the waiting time of vehicles. The approach was implemented using Python, and SUMO (Simulation of Urban Mobility), the system adapts to fluctuating traffic patterns and provides an efficient solution for urban traffic management.