Deep Reinforcement Learning for Traffic Flow Optimization in Urban Planning

Abstract

Traffic jam becomes a major problem on a worldwide scale these days. The number of vehicles has risen considerably, but the capacity of roadways and transportation infrastructure to manage the additional congestion has not yet increased proportionately. Public health and society are suffering as a result of the rise in road congestion and pollution caused by traffic. In order to minimize traffic congestion, this research develops a revolutionary reinforcement learning (RL)-based approach. Researcher have created a novel deep Q network (DQN) and seamlessly incorporated it into the system. The applied DQL model cut the length of traffic waits by 50% and made each lane more appealing by 10%. The outcomes show that the approach is successful at establishing strict standards for reducing traffic. The proposed system shows that that RL can efficiently control traffic traffic snarl-up in cities quickly without compromise in the environmentally friendly nature. Added on utilizing RL may considerably ease traffic snarl-up in cities.