Adaptive traffic light control using deep reinforcement learning technique

Smart city growth needs information and communication technology to increase urban sustainability but faces critical traffic congestion and vehicle classification issues. It is crucial to dynamically change the traffic light on the road network to reduce the delay of vehicles and avoid congestion in the smart city. Modifying the traffic light should be adaptive, considering the number of vehicles on the road and the options available to route the vehicles toward their destination. Our scheme is the first proposed model based on deep learning to solve the problem of traffic congestion in the urban environment. This model classifies the vehicle’s type on the road and assigns different vehicle weights. We assign 0.0 for no vehicles, and 1.0, 2.0, 3.0 for light-weight, moderate-weight, and heavy-weight vehicles respectively. The proposed work has trained using experience replay and target network based on a deep double-Q learning mechanism. Our resultant model applies in a real-time traffic network that uses Dedicated-Short-Range-Communication (DSRC) protocol for wireless communication. The simulation of this work uses SUMO (Simulation in Urban MObility) with the data generated on SUMO using a random function. The results show that the traffic light of a certain traffic intersection becomes adaptive, aligning with the goals mentioned above. The proposed model efficiently reduces the average waiting time up to 91.7% at the intersection points of the road which is shown in the graph in the result section.