CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models

• URL: http://arxiv.org/abs/2402.06127v1
• Date: Fri, 9 Feb 2024 01:19:41 GMT
• Title: CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models
• Abstract summary: CityFlowER is an advanced simulator for efficient and realistic city-wide traffic simulation. It pre-embeds Machine Learning models within the simulator, eliminating the need for external API interactions. It offers unparalleled flexibility and efficiency, particularly in large-scale simulations.
• Score: 25.567208505574072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional traffic simulators, such as SUMO and CityFlow, are often limited by their reliance on rule-based models with hyperparameters that oversimplify driving behaviors, resulting in unrealistic simulations. To enhance realism, some simulators have provided Application Programming Interfaces (APIs) to interact with Machine Learning (ML) models, which learn from observed data and offer more sophisticated driving behavior models. However, this approach faces challenges in scalability and time efficiency as vehicle numbers increase. Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation. CityFlowER innovatively pre-embeds ML models within the simulator, eliminating the need for external API interactions and enabling faster data computation. This approach allows for a blend of rule-based and ML behavior models for individual vehicles, offering unparalleled flexibility and efficiency, particularly in large-scale simulations. We provide detailed comparisons with existing simulators, implementation insights, and comprehensive experiments to demonstrate CityFlowER's superiority in terms of realism, efficiency, and adaptability.

Related papers

• RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning [19.985714275376726]
  We introduce RLFTSim, a reinforcement-learning-based fine-tuning framework.<n>It enhances scenario realism by aligning simulator rollouts with real-world data distributions.<n>We also demonstrate the effectiveness of our approach for distilling traffic simulation controllability through goal conditioning.
  arXiv  Detail & Related papers  (2026-05-18T18:57:13Z)
• Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning [20.72669976554826]
  Learned Graph Network Simulators (GNSs) offer a promising alternative to traditional mesh-based physics simulators.<n>Their speed and inherent differentiability make them well suited for applications that require fast and accurate simulations, such as robotic manipulation or manufacturing optimization.<n>We utilize movement primitives to directly predict fast, stable and accurate simulations from a single model call.
  arXiv  Detail & Related papers  (2025-11-07T13:34:02Z)
• Simulation Priors for Data-Efficient Deep Learning [56.525770511247934]
  SimPEL is a method that efficiently combines first-principles models with data-driven learning.<n>We evaluate SimPEL on diverse systems, including biological, agricultural, and robotic domains.<n>For decision-making, we demonstrate that SimPEL bridges the sim-to-real gap in model-based reinforcement learning.
  arXiv  Detail & Related papers  (2025-09-06T14:36:41Z)
• InfGen: Scenario Generation as Next Token Group Prediction [49.54222089551598]
  InfGen is a scenario generation framework that outputs agent states and trajectories in an autoregressive manner.<n>Experiments demonstrate that InfGen produces realistic, diverse, and adaptive traffic behaviors.
  arXiv  Detail & Related papers  (2025-06-29T16:18:32Z)
• A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking [23.04575933073716]
  We propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation and optimization. The simulator can iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with 2,464,950 vehicles. We choose five representative scenarios and benchmark classical rule-based algorithms, reinforcement learning algorithms, and black-box optimization algorithms.
  arXiv  Detail & Related papers  (2024-06-15T14:58:17Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• TransWorldNG: Traffic Simulation via Foundation Model [23.16553424318004]
  We present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph Computing techniques to learn traffic dynamics from real data. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators.
  arXiv  Detail & Related papers  (2023-05-25T05:49:30Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• BITS: Bi-level Imitation for Traffic Simulation [38.28736985320897]
  We take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs. We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets. As part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets.
  arXiv  Detail & Related papers  (2022-08-26T02:17:54Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.