Approach to Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

Smart mobility intelligent traffic services have become critical in intelligent transportation systems (ITS). This involves using advanced sensors and controllers and the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. Deep reinforcement learning (DRL) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. On the other hand, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers in the literature. Hence, using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.