mlsanityData to 14 Oct 2026

LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research

arXiv 2602.21852 ↗First posted 25 Feb 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Reinforcement learning for traffic signal control is bottlenecked by simulators: training in SUMO takes hours, reproducing results often requires days of platform-specific setup, and the slow iteration cycle discourages the multi-seed experiments that rigorous evaluation demands. Much of this cost is unnecessary, since for signal timing optimization the relevant dynamics are queue formation and discharge, which the Cell Transmission Model (CTM) captures as a macroscopic flow model. We introduce LightSim, a pure Python, pip-installable traffic simulator with Gymnasium and PettingZoo interfaces that runs over 20000 steps per second on a single CPU. Across cross-simulator experiments spanning single intersections, grid networks, arterial corridors, and six real-world city networks, LightSim preserves controller rankings from SUMO for both classical and reinforcement learning strategies while training 3 to 7 times faster. LightSim is released as an open-source benchmark with nineteen built-in scenarios, seven controllers, and full reinforcement learning pipelines, lowering the barrier to signal control research from days to minutes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 27 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

9 of 27 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  3. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  4. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  5. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  6. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  7. 2020AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Controlcited by 62
  8. 2021A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehiclescited by 9
  9. 2026Hierarchical GNN-Based Multi-Agent Learning for Dynamic Queue-Jump Lane and Emergency Vehicle Corridor Formationcited by 3

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  1. 2026Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
  2. 2024MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
  3. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  4. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  5. 2024PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
  6. 2024MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
  7. 2025Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study
  8. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner