# Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion

arXiv: https://arxiv.org/abs/2408.15751 · HTML: https://mlsanity.com/paper/2408.15751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning](https://mlsanity.com/paper/1704.08883/) (2017)
- [Using a Deep Reinforcement Learning Agent for Traffic Signal Control](https://mlsanity.com/paper/1611.01142/) (2016)
- [Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network](https://mlsanity.com/paper/1705.02755/) (2017)

## Built on by (most cited first, 1 of 1)

- [Model Context Protocols in Adaptive Transport Systems: A Survey](https://mlsanity.com/paper/2508.19239/) (2025)

## Abstract (arXiv)

The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traffic management systems, particularly through adaptive traffic signal control, offers a promising solution. This paper explores the use of Reinforcement Learning (RL) to enhance traffic signal operations at intersections, aiming to reduce congestion without extensive sensor networks. We introduce two RL-based algorithms: a turn-based agent, which dynamically prioritizes traffic signals based on real-time queue lengths, and a time-based agent, which adjusts signal phase durations according to traffic conditions while following a fixed phase cycle. By representing the state as a scalar queue length, our approach simplifies the learning process and lowers deployment costs. The algorithms were tested in four distinct traffic scenarios using seven evaluation metrics to comprehensively assess performance. Simulation results demonstrate that both RL algorithms significantly outperform conventional traffic signal control systems, highlighting their potential to improve urban traffic flow efficiently.