In this paper, methods have been explored to effectively optimise traffic signal control to minimise waiting times and queue lengths, thereby increasing traffic flow. The traffic intersection was first defined as a Markov Decision Process, and a state representation, actions and rewards were chosen. Simulation of Urban MObility (SUMO) was used to simulate an intersection and then compare a Round Robin Scheduler, a Feedback Control mechanism and two Reinforcement Learning techniques - Deep Q Network (DQN) and Advantage Actor-Critic (A2C), as the policy for the traffic signal in the simulation under different scenarios. Finally, the methods were tested on a simulation of a real-world intersection in Bengaluru, India.
@article{arxiv.2109.00937,
title = {A Comparative Study of Algorithms for Intelligent Traffic Signal Control},
author = {Hrishit Chaudhuri and Vibha Masti and Vishruth Veerendranath and S Natarajan},
journal= {arXiv preprint arXiv:2109.00937},
year = {2022}
}