# An Open-Source Framework for Adaptive Traffic Signal Control

arXiv: https://arxiv.org/abs/1909.00395 · HTML: https://mlsanity.com/paper/1909.00395/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles](https://mlsanity.com/paper/2104.10340/) (2021)
- [Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2103.16223/) (2021)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [Back to Basics: Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2109.07180/) (2021)
- [A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers](https://mlsanity.com/paper/2101.09614/) (2021)
- [A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists](https://mlsanity.com/paper/2311.13905/) (2023)

## Abstract (arXiv)

Sub-optimal control policies in transportation systems negatively impact mobility, the environment and human health. Developing optimal transportation control systems at the appropriate scale can be difficult as cities' transportation systems can be large, complex and stochastic. Intersection traffic signal controllers are an important element of modern transportation infrastructure where sub-optimal control policies can incur high costs to many users. Many adaptive traffic signal controllers have been proposed by the community but research is lacking regarding their relative performance difference - which adaptive traffic signal controller is best remains an open question. This research contributes a framework for developing and evaluating different adaptive traffic signal controller models in simulation - both learning and non-learning - and demonstrates its capabilities. The framework is used to first, investigate the performance variance of the modelled adaptive traffic signal controllers with respect to their hyperparameters and second, analyze the performance differences between controllers with optimal hyperparameters. The proposed framework contains implementations of some of the most popular adaptive traffic signal controllers from the literature; Webster's, Max-pressure and Self-Organizing Traffic Lights, along with deep Q-network and deep deterministic policy gradient reinforcement learning controllers. This framework will aid researchers by accelerating their work from a common starting point, allowing them to generate results faster with less effort. All framework source code is available at https://github.com/docwza/sumolights.