An Artificial Intelligence Approach to Manage Vehicles Motorway Entries in Congested Traffic

Efficient traffic management in high-density urban areas is a critical challenge, specifically in highway entry ramps and merging points. This paper deals with the problem of managing vehicles that have to enter highway ramps and intersections. The problem is formulated as an optimization task to minimize sudden braking, waiting times, and collision risks. A Deep Reinforcement Learning approach based on the ActorCritic framework is proposed to train the intelligent traffic light agents managing the vehicle entries. The reward function is designed to dynamically balance safety and efficiency by reducing braking events and congestion. A simulation campaign is performed in a high-density merging scenario located in central-northern Italy at the intersection between the A1 Highway and the A14 Motorway. The results demonstrate the benefits obtained in reduced traffic flow and increased safety.