Traffic Signal Control

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 21

All topics

EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs
Oct 7, 2026Leizhen Wang, Peibo Duan, Zhenlin Qin +5Traffic Signal Control Automated Heuristic Design
Monash University · KTH Royal Institute of Technology · Chinese Academy of Sciences +2
Prediction is Better than Detection: Traffic Congestion Control using Drones
Sep 30, 2026Samira Hayat, Christian RaffelsbergerMulti-Robot Systems Traffic Signal Control
Lakeside Labs GmbH Klagenfurt, Austria
VLALight: A Vision-Language-Action Model for Traffic Signal Control
Sep 29, 2026Pan Zhang, Siqi Lai, Kemu Dong +1Intelligent Transportation Systems Vision-Language-Action Models
The Hong Kong University of Science and Technology (Guangzhou) · Dalian University of Technology
Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
Sep 1, 2026Ferdous Al Rafi, Susrik Mukherjee, Latika Liladhar Dekate +6RL Benchmarks Sim-to-Real Transfer
Arizona State University
A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks
Jul 23, 2026Bertil BraunIntelligent Transportation Systems Traffic Signal Control
HiLLTS: Zero-Shot Hierarchical LLM-Guided Traffic Signal Control for Sustainable Transportation
Jul 17, 2026Yue Ding, Tendai Mukande, Mingming LiuLanguage Model-Based Control Intelligent Transportation Systems
Research Ireland Centre for Researching Training in Machine Learning at Dublin City University
Explainable Reinforcement Learning for Adaptive Traffic Signal Control
Jul 4, 2026Dickens Kwesiga, Nishu Choudhary, Angshuman Guin +1Reinforcement Learning Traffic Signal Control
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning
Jun 29, 2026Qian Hu, Haoyang Peng, Songan Zhang +2Diffusion-Based Planning Intelligent Transportation Systems
Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
Jun 20, 2026Yousef AlSaqabiReinforcement Learning Intelligent Transportation Systems
Department of Electrical Engineering, Kuwait University, Kuwait
Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
May 31, 2026Dénes Toth, George Ambroladze, Edwin Sundberg +2Intelligent Transportation Systems Active Inference
Department of Computer Systems and Sciences, Stockholm University, Stockholm 164 25, Sweden
Momentum Based Reward Design for Low Emission Traffic Signal Control
May 28, 2026Chinmay Mundane, Amith Manoharan, Arun Kumar SinghReinforcement Learning Reward Shaping
Institute of Technology, University of Tartu
ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control
May 28, 2026Aoyu Pang, Maonan Wang, Yuejiao Xie +3RL Control Intelligent Transportation Systems
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong · Shanghai AI Laboratory, Shanghai, China +1
OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
May 24, 2026Mingyuan Li, Boyang Huang, Tianqi Jiang +5Intelligent Transportation Systems Traffic Signal Control
University of Electronic Science and Technology of China · Beijing Xiaocheng Intelligent Computing · Lanzhou University
DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learning
May 20, 2026Bibek Poudel, Lei Zhu, Kevin Heaslip +2RL Control Intelligent Transportation Systems
University of Tennessee, Knoxville, TN, USA · University of North Carolina at Charlotte, Charlotte, NC, USA
Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
May 11, 2026Matteo Cederle, Giacomo Scatto, Gian Antonio SustoReinforcement Learning Intelligent Transportation Systems
University of Padova, Italy
OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
May 8, 2026Darryl Jacob, Xinyu Liu, Muchao Ye +2Reward Shaping Language Model-Based Control
Department of Computer Science and Software Engineering Auburn University · Department of Computer Science2026 University of Iowa · Department of Electrical and Computer EngineeringMay Clemson University
Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making
Apr 30, 2026Salman Jan, Toqeer Ali Syed, Shahid Kamal +2Intelligent Transportation Systems Traffic Signal Control
Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia · Faculty of Computer Studies, Arab Open University-Bahrain, A’Ali, Bahrain · Faculty of Computer and Information System, Islamic University of Madinah, Madinah, Saudi Arabia +1
No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
Apr 28, 2026Anas Gamal Aly, Hala ElAaragIntelligent Transportation Systems Traffic Signal Control
Stetson University DeLand, Florida, USA
DGLight: DQN-Guided GRPO Fine-Tuning of Large Language Models for Traffic Signal Control
Apr 28, 2026Chenbo YuLanguage Model-Based Control Intelligent Transportation Systems
National University of Singapore Singapore
LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support
Apr 26, 2026Jiazhao ShiLanguage Model-Based Control Safety-Critical Control
Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
Apr 19, 2026Siqi Lai, Pan Zhang, Yuping Zhou +3Agentic RL Language Model-Based Control
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China · Shandong University, Jinan, Shandong, China

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs

Leizhen Wang, Peibo Duan, Zhenlin Qin, Yancheng Ling+4

Monash University · KTH Royal Institute of Technology · Chinese Academy of Sciences+2

Traffic Signal Control Automated Heuristic Design LLM-Based Program Synthesis Program Synthesis Evolutionary Optimization

Open paper AskPDF arXiv

Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control objectives. However, adapting existing strategies often involves repeated manual design and adjustment, making it difficult to systematically explore better control rules for a target network. Large language models (LLMs) can automate this process, but directly using them to select signal phases leaves decision rules embedded in black-box models and incurs recurring inference costs and latency. This paper formulates TSC as a modular program design problem and proposes EvoSignal, an LLM-guided evolutionary framework using traffic knowledge and performance feedback. The modular representation separates traffic feature extraction, local phase prioritization, and optional network-based priority adjustment. Starting from several established strategies, EvoSignal improves programs through feedback on congestion and signal operation, retaining strategies with different performance trade-offs. The resulting programs operate without online LLM inference. Simulation experiments across five scenarios on two real-world road networks show that the selected default EvoSignal program reduces waiting time by 16.8--49.2% relative to the lowest waiting time achieved by the 20 conventional, reinforcement learning-based, and LLM-based baselines in each scenario. A program prioritizing travel time and queue length outperforms all 20 baselines on all three metrics in the search scenario and remains among the top three on each metric when transferred unchanged to the other four scenarios. These findings support automated design of inspectable control programs that transfer across the evaluated road networks and traffic demands.Code is available at https://github.com/georgewanglz2019/EvoSignal.

Momentum

Related topics

Latest papers 21