# Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice

arXiv: https://arxiv.org/abs/2309.09881 · HTML: https://mlsanity.com/paper/2309.09881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [InTAS -- The Ingolstadt Traffic Scenario for SUMO](https://mlsanity.com/paper/2011.11995/) (2020)
- [Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control](https://mlsanity.com/paper/1807.01628/) (2018)
- [CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.13143/) (2022)
- [Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2005.08245/) (2020)

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

- [Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach](https://mlsanity.com/paper/2312.07795/) (2023)

## Abstract (arXiv)

Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle-to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. Learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems.