# Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method

arXiv: https://arxiv.org/abs/2306.08843 · HTML: https://mlsanity.com/paper/2306.08843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning](https://mlsanity.com/paper/1704.08883/) (2017)
- [Diagnosing Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/1905.04716/) (2019)
- [Learning an Interpretable Traffic Signal Control Policy](https://mlsanity.com/paper/1912.11023/) (2019)
- [Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization](https://mlsanity.com/paper/1909.10651/) (2019)
- [Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method](https://mlsanity.com/paper/2204.12190/) (2022)
- [Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning](https://mlsanity.com/paper/1711.10941/) (2017)
- [SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2305.16145/) (2023)
- [Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion](https://mlsanity.com/paper/1903.02589/) (2019)
- [A Polynomial-time Decentralised Algorithm for Coordinated Management of Multiple Intersections](https://mlsanity.com/paper/2205.00877/) (2022)
- [Using Bi-Directional Information Exchange to Improve Decentralized Schedule-Driven Traffic Control](https://mlsanity.com/paper/1907.01978/) (2019)
- [Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming](https://mlsanity.com/paper/2007.06639/) (2020)

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

- [Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2503.11488/) (2025)

## Abstract (arXiv)

Efficient traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. Key to the challenge of TSC includes 1) the essential of real-time signal decision, 2) the complexity in traffic dynamics, and 3) the network-level coordination. Recent efforts that applied reinforcement learning (RL) methods can query policies by mapping the traffic state to the signal decision in real-time, however, is inadequate for unexpected traffic flows. By observing real traffic information, online planning methods can compute the signal decisions in a responsive manner. We propose an explicit multiagent coordination (EMC)-based online planning methods that can satisfy adaptive, real-time and network-level TSC. By multiagent, we model each intersection as an autonomous agent, and the coordination efficiency is modeled by a cost (i.e., congestion index) function between neighbor intersections. By network-level coordination, each agent exchanges messages with respect to cost function with its neighbors in a fully decentralized manner. By real-time, the message passing procedure can interrupt at any time when the real time limit is reached and agents select the optimal signal decisions according to the current message. Moreover, we prove our EMC method can guarantee network stability by borrowing ideas from transportation domain. Finally, we test our EMC method in both synthetic and real road network datasets. Experimental results are encouraging: compared to RL and conventional transportation baselines, our EMC method performs reasonably well in terms of adapting to real-time traffic dynamics, minimizing vehicle travel time and scalability to city-scale road networks.