Implementation of a Real-Time Analysis Simulation System for Traffic Signal Control Algorithms to Reduce Vehicle Exhaust Emissions and Improve Traffic Flow

Traffic signal control (TSC) is a part of intelligent transportation systems to reduce traffic congestion and emissions. Recently, dynamic traffic signal control systems using artificial intelligence and reinforcement learning have been studied to achieve these goals. Although verification of TSC algorithms is ideally performed in a real environment, traffic simulation tools are widely used due to safety issues. However, existing simulation tools have limitations in real-time result analysis, making it difficult to analyze the strengths and weaknesses of each algorithm according to each traffic situation. In this paper, we propose RTASS, a real-time analysis simulation system for TSC algorithms that enables real-time comparison and result analysis, to improve the problems of such a simulation environments. To validate RTASS, we experiment using actual intersection data. As a result of comparing the traffic volume of the actual intersection data and the simulation, we confirmed that the error rate was maintained within 2% on average. In addition, we evaluate the performance of each TSC algorithm using RTASS and verify the results by comparing two existing methods and two proposed TSC methods. We confirmed that real-time comparison of traffic analysis and algorithms is possible using the proposed simulation system.