The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment

Abstract

As the population and vehicle ownership increase, emission of pollutants is also increasing. The percentage of GHG emission by transportation sector is about 21 % in 2015 (OECD), and this may be caused by frequent stop-and-go phenomenon or delay time of vehicles in signalized intersection. Generally, these could be minimised by driving in constant speed or decreasing the delay times with an efficient traffic signal control. On the other hand, researches try to decrease vehicles’ delay time and to exclude the unnecessary stop-and-go phenomenon in an urban signalized intersection with an advent of V2X (Vehicle-to-Everything) technology development. Especially, in traditional pre-timed traffic signal control situation, even the autonomous vehicles would be impossible to exhibit their own maximum performance. Thus, the development of the traffic signal control system could have effects not only on the traffic flow but also on environmental aspects, which optimizes the signalized traffic flow based on the real-time vehicle information. In this research, on the premise of V2X environment, changes in traffic flow and the emission are analysed based on microscopic traffic information. In specific, the reinforcement learning model is constructed based on Deep Learning which learns the real-time traffic information and displays the optimal traffic signal. The performance of the system was analysed through microscopic traffic simulator - Vissim. The proposed system is expected to contribute on analysing the traffic flow and the environmental effects. Also, it is expected to contribute on constructing the green smart cities with an advent of autonomous vehicle operation in future V2X environment.