Optimized Cooperative Routing for Autonomous Vehicles

Current traffic control systems — comprising traffic lights, signs, and right-of-way rules — are often inadequate, leading to accidents, excessive fuel consumption, and unnecessary delays. Three key scenarios contribute to these inefficiencies. First, drivers may run red lights due to a lack of traffic signal timing information, leading to indecision when encountering a yellow light, a major cause of accidents. Second, abrupt speed changes in response to traffic signals force drivers to brake suddenly, increasing fuel consumption and travel time. For instance, a driver may accelerate at a green light only to encounter a red light shortly after, resulting in inefficient fuel use. Lastly, vehicles often remain stopped at red lights despite no cross-traffic, leading to wasted fuel and time.This study simulates these scenarios using the Eclipse SUMO tool, with results aligning with expected inefficiencies. The problem is mathematically modeled using Pyomo, and a centralized optimization approach is applied to enhance traffic synchronization and efficiency. By dynamically calculating vehicle velocities based on real-time traffic data, the study proposes an optimized, traffic light-free system. The results demonstrate improved fuel efficiency, reduced accidents, and minimized delays, highlighting the potential of centralized optimization in modern traffic management.