Adaptive Traffic Signal Cycle Control for Green City Traffic Management

Traffic congestion at road intersections is one of the critical challenges for vehicular traffic systems. In the urban environment, this traffic congestion results in an increase in the travel time, more driver fatigue, higher pollution, etc. Consequently, traffic congestion not only exacerbates travel delays but also impacts the environment and human health. As a consequence, the implementation of adaptive traffic signal cycle control to alleviate traffic congestion is of paramount importance. In this work, we use Simulation of Urban MObility (SUMO) based traffic simulation to control traffic signal cycle for smart cities. We propose a deep reinforcement learning (DRL)-based adaptive traffic signal cycle control that aims to minimize carbon dioxide (CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) emissions while simultaneously decreasing the waiting time of the drivers. The results showcase that with the use of a DRL-based adaptive traffic signal control cycle, specifically, the average waiting time of the vehicles increases by 15.58% as the number of vehicles increases from 1750 to 2000. Furthermore, the average CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions of the vehicles increase by 25% as the number of vehicles increases from 1750 to 2000.