# Decentralized Signal Control for Urban Road Networks

arXiv: https://arxiv.org/abs/1310.0491 · HTML: https://mlsanity.com/paper/1310.0491/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2013.

## Built on by (most cited first, 7 of 7)

- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [Learning and Information in Stochastic Networks and Queues](https://mlsanity.com/paper/2105.08769/) (2021)
- [Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization](https://mlsanity.com/paper/2201.00006/) (2021)
- [Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2411.07271/) (2024)
- [CycLight: learning traffic signal cooperation with a cycle-level strategy](https://mlsanity.com/paper/2401.08121/) (2024)
- [DynLight: Realize dynamic phase duration with multi-level traffic signal control](https://mlsanity.com/paper/2204.03471/) (2022)
- [A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach](https://mlsanity.com/paper/2508.20102/) (2025)

## Abstract (arXiv)

We propose in this paper a decentralized traffic signal control policy for urban road networks. Our policy is an adaptation of a so-called BackPressure scheme which has been widely recognized in data network as an optimal throughput control policy. We have formally proved that our proposed BackPressure scheme, with fixed cycle time and cyclic phases, stabilizes the network for any feasible traffic demands. Simulation has been conducted to compare our BackPressure policy against other existing distributed control policies in various traffic and network scenarios. Numerical results suggest that the proposed policy can surpass other policies both in terms of network throughput and congestion.