# A Comparative Study of Algorithms for Intelligent Traffic Signal Control

arXiv: https://arxiv.org/abs/2109.00937 · HTML: https://mlsanity.com/paper/2109.00937/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions](https://mlsanity.com/paper/2310.13129/) (2023)
- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)
- [Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies](https://mlsanity.com/paper/2505.14544/) (2025)

## Abstract (arXiv)

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.