A Comparative Study of Algorithms for Intelligent Traffic Signal Control
This addresses traffic congestion for urban planners and commuters, but it is incremental as it builds on existing reinforcement learning techniques.
The study compared algorithms for traffic signal control to reduce waiting times and queue lengths, finding that reinforcement learning methods like DQN and A2C outperformed traditional approaches in simulations, with DQN reducing average waiting time by 30% compared to Round Robin.
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.