mlsanityData to 7 Oct 2026

CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation

arXiv 2210.00896 ↗First posted 3 Oct 2022physics.soc-ph cs.AI cs.CYcited by 6

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Abstract (arXiv)

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present City Brain Lab, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~urlhttps://github.com/CityBrainLab/CityBrainLab.git.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

12 of 30 references.

References · 12

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 133
  3. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  4. 2019Learning Phase Competition for Traffic Signal Controlcited by 84
  5. 2019A Survey on Traffic Signal Control Methodscited by 78
  6. 2020AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Controlcited by 60
  7. 2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 46
  8. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  9. 2021Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlcited by 28
  10. 2020GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learningcited by 17
  11. 2021DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Controlcited by 11
  12. 2020QarSUMO: A Parallel, Congestion-optimized Traffic Simulatorcited by 1

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024Graph Data Condensation via Self-expressive Graph Structure Reconstructioncited by 18
  2. 2024Dataset Condensation for Time Series Classification via Dual Domain Matchingcited by 8
  3. 2024A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulationcited by 3
  4. 2025Towards Physics-informed Diffusion for Anomaly Detection in Trajectoriescited by 2
  5. 2024A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarkingcited by 0
  6. 2025A Pretrained Probabilistic Transformer for City-Scale Traffic Volume Predictioncited by 0