Leveraging Edge Computing for Real-Time Traffic Management and Simulation with SUMO in Smart Cities

Sammanfattning

Traffic congestion is a persistent problem in modern urban environments, causing delays, increasing pollution, and negatively affecting the quality of life of millions of city dwellers. As urban populations grow, the need for intelligent traffic man- agement systems becomes more pressing. This thesis presents a novel approach to real-time traffic management using edge computing in conjunction with the Urban Mobility Simulation (SUMO) tool. The Differential Evolution (DE) algo- rithm is applied to optimize traffic signal timings, with the goal of minimizing vehicle delays at multiple intersections within a simulated smart city. Using edge computing, traffic data is processed locally, enabling faster responses and dynamic adjustments to signal timings. The results of this approach demonstrate a sig- nificant improvement in traffic flow efficiency, with reductions in vehicle delays in various traffic scenarios. This research highlights the potential for combining edge computing with evolutionary algorithms to address traffic congestion challenges in modern cities.