Decentralized Signal Control for Urban Road Networks
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2013.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
7 papers.
- 2021Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlcited by 28
- 2021Learning and Information in Stochastic Networks and Queuescited by 8
- 2021Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimizationcited by 7
- 2024Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learningcited by 2
- 2024CycLight: learning traffic signal cooperation with a cycle-level strategycited by 1
- 2022DynLight: Realize dynamic phase duration with multi-level traffic signal controlcited by 0
- 2025A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approachcited by 0
- 2025A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach12 Aug 2025
- 2024Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning10 Nov 2024
- 2024CycLight: learning traffic signal cooperation with a cycle-level strategy16 Jan 2024
- 2022DynLight: Realize dynamic phase duration with multi-level traffic signal control7 Apr 2022
- 2021Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization30 Dec 2021
- 2021Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control19 Dec 2021
- 2021Learning and Information in Stochastic Networks and Queues18 May 2021