Adaptive traffic signal control in multiple intersections network

Abstract Traffic in urban areas is generally regulated by traffic lights, which represent a traffic flow bottleneck if they are not effectively configured. The dynamic nature of traffic flows makes reliable prediction possible only over a limited time horizon, and hence necessitates continual re-computation solutions. As intersections network size increases, the exponential growth in joint signal timing and traffic states exhibit a computational barrier to determining effective traffic lights timing. So, real-time traffic signal control optimization becomes a challenging problem. The present work investigates the issue of adaptive traffic control using real-time traffic data in multiple intersections. Each network intersection is controlled by an autonomous agent operating with a view of incoming traffic. The generated model presents ability to identifying dynamic changes in traffic flow conditions as well as adjusting green lights sequences and offering a good coordination between each intersections neighboring. The main contribution of this model is a real-time adaptive control of the traffic lights, according to discontinuity and heterogeneity of traffic flow. Simulation results confirm the system effectiveness compared to fixed time control, actuated time control and green wave method. And that by mitigating the congestion in terms of maximum throughput traffic flow, minimum waiting time and stops while maintaining fairness among all the network traffic lights.