Adjusting Street Plans Using Deep Reinforcement Learning

Abstract

Traffic flow optimization is an active line of research despite the wealth of literature been written on the topic, the major problem is the high dimension of input information that is available for controlling the traffic lights agents at each scenario, by the information we mean the traffic data that is continuously sampled by traffic cameras and detectors. All the papers came out focused on controlling the traffic lights cycle taking the street plans as a given. Controlling a traffic light cycle for a street plan that does not solve the population demand distribution will not end traffic congestion completely. Because of the inability to build new streets and a continuously changing population demand, the only thing to change is the streets plan. So This study proposes the idea of controlling the directions of these streets (one-way, two-ways) to match the new transportation demands of the ever-changing population in an area a task that is easy to do by using deep reinforcement learning. Deep Reinforcement learning combines both the generalization of reinforcement learning to any new scenario and the ability to handle large input spaces and convergences to minima to deep learning, since the action space in the study is discrete space-streets directions - we chose to use Deep Q-Networks - DQN - several experiments are performed on 4 different SUMO - Simulation of Urban Mobility - simulation networks.