A Learning Approach for Road Traffic Optimization in Urban Environments

Abstract

In many urban areas where road drivers are suffering from the huge road traffic flow, conventional traffic management methods have become inefficient. One alternative is to let road-side units or vehicles learn how to calculate the optimal path based on the traffic situation. This work aims to provide the optimal path in terms of travel time for the vehicles seeking to reach their destination avoiding road traffic congestion and in the least possible time. In this paper we apply a reinforcement learning technique, in particular Q-learning, that is employed to learn the best action to take in different situations, where the transiting delay from a state to another is used to determinate the rewards. The simulation results confirm that the proposed Q-learning approach outperformed the greedy existing algorithm and present better performances.