Intelligent Traffic Management System for Smart Cities Utilizing Reinforcement Learning Algorithm

Abstract

Traffic congestion on roadways or at intersections can occur for a variety of reasons, including increased vehicle queues, slow driving, and so on. When multiple emergencies occur at the same time, traffic gridlock may occur. An adaptive traffic control system is a traffic management method that allows signals to adapt to the current traffic demand in real time. Adaptive traffic signals work by synchronizing software and hardware. Q-learning necessitates the creation of accurate natural environment forms prior to taking action. Instead, a dynamic communication system was developed to explore the interplay between state, action, and rewards in that particular setting. The current traffic signal functions are based on predetermined traffic flow information to extract short time expectations, making it easier to calculate the signal management system's conclusion. If a certain model is used, then customizable traffic light management agents will have to collect photos of the current traffic situation and generate control signals at regular intervals. To improve the algorithm's stability, replay, occurrence, and ideal techniques have been implemented. The primary goal of the algorithm is to regulate congested traffic, which is accomplished by combining the dynamic network with the linear signal layout.