Integration of Speed Control and Parking Management in SUMO for Urban Traffic Optimization

This study presents an intelligent system for dynamic urban traffic management, focused on the automatic adjustment of speed limits and the activation of lanes as parking areas to optimize urban traffic. Using realistic scenarios simulated in the SUMO (Simulation of Urban MObility) platform and controlled by a neural network implemented in Python, the system effectively responds to variable conditions such as vehicle density, traffic flow, and other factors. To evaluate the achieved optimization, the results are compared using various traffic performance indicators, including congestion levels, average speed, travel time, among others. Through adaptive real-time behavior, the system achieves greater traffic fluidity, reduced congestion, and better use of available infrastructure. The designed control system significantly improves performance compared to uncontrolled scenarios: on average, it reduces travel time by 16 %, CO₂ emissions by 13 %, waiting time by 12 %, and accident probability by 15 %, while traffic flow increases by 11 %. The results show that this approach can effectively complement traditional traffic light control and adapt to diverse urban contexts, positioning itself as a promising solution to improve mobility in modern cities.