# Efficient Pressure: Improving efficiency for signalized intersections

arXiv: https://arxiv.org/abs/2112.02336 · HTML: https://mlsanity.com/paper/2112.02336/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)

## Built on by (most cited first, 24 of 24)

- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems](https://mlsanity.com/paper/2508.02344/) (2025)
- [DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback](https://mlsanity.com/paper/2306.07553/) (2023)
- [DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data](https://mlsanity.com/paper/2410.22938/) (2024)
- [Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach](https://mlsanity.com/paper/2312.07795/) (2023)
- [Learning Traffic Signal Control via Genetic Programming](https://mlsanity.com/paper/2403.17328/) (2024)
- [GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy](https://mlsanity.com/paper/2508.16090/) (2025)
- [Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2503.20205/) (2025)
- [A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control](https://mlsanity.com/paper/2511.00136/) (2025)
- [CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2603.24366/) (2026)
- [Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control](https://mlsanity.com/paper/2502.13248/) (2025)
- [Evolutionary Discovery of Heuristic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.23122/) (2025)
- [DynLight: Realize dynamic phase duration with multi-level traffic signal control](https://mlsanity.com/paper/2204.03471/) (2022)
- [A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking](https://mlsanity.com/paper/2406.10661/) (2024)
- [Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control](https://mlsanity.com/paper/2412.16225/) (2024)
- [Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models](https://mlsanity.com/paper/2501.02548/) (2025)
- [Using a single actor to output personalized policy for different intersections](https://mlsanity.com/paper/2503.07678/) (2025)
- [A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control](https://mlsanity.com/paper/2503.14250/) (2025)
- [A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning](https://mlsanity.com/paper/2509.03118/) (2025)
- [CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control](https://mlsanity.com/paper/2604.05663/) (2026)
- [ProcessLight: Process Supervision for Large Language Model Based Traffic Signal Control](https://mlsanity.com/paper/2609.22746/) (2026)
- [VLALight: A Vision-Language-Action Model for Traffic Signal Control](https://mlsanity.com/paper/2609.36934/) (2026)
- [EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs](https://mlsanity.com/paper/2610.09563/) (2026)

## Abstract (arXiv)

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem. However, existing RL-based methods are rarely deployed considering that they are neither cost-effective in terms of computing resources nor more robust than traditional approaches, which raises a critical research question: how to construct an adaptive controller for TSC with less training and reduced complexity based on RL-based approach? To address this question, in this paper, we (1) innovatively specify the traffic movement representation as a simple but efficient pressure of vehicle queues in a traffic network, namely efficient pressure (EP); (2) build a traffic signal settings protocol, including phase duration, signal phase number and EP for TSC; (3) design a TSC approach based on the traditional max pressure (MP) approach, namely efficient max pressure (Efficient-MP) using the EP to capture the traffic state; and (4) develop a general RL-based TSC algorithm template: efficient Xlight (Efficient-XLight) under EP. Through comprehensive experiments on multiple real-world datasets in our traffic signal settings' protocol for TSC, we demonstrate that efficient pressure is complementary to traditional and RL-based modeling to design better TSC methods. Our code is released on Github.