A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking

• URL: http://arxiv.org/abs/2406.10661v1
• Date: Sat, 15 Jun 2024 14:58:17 GMT
• Title: A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking
• Authors: Jun Zhang, Wenxuan Ao, Junbo Yan, Depeng Jin, Yong Li,
• Abstract summary: We propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation. The simulator is able to iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with more than a million vehicles.
• Score: 23.04575933073716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of artificial intelligence techniques, transportation system optimization is evolving from traditional methods relying on expert experience to simulation and learning-based decision optimization methods. Learning-based optimization methods require extensive interaction with highly realistic microscopic traffic simulators for optimization. However, existing microscopic traffic simulators are computationally inefficient in large-scale scenarios and therefore significantly reduce the efficiency of the data sampling process of optimization algorithms. In addition, the optimization scenarios supported by existing simulators are limited, mainly focusing on the traffic signal control. To address these challenges and limitations, we propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation. The simulator is able to iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with more than a million vehicles compared to the best baseline. Based on the simulator, we implement a set of microscopic and macroscopic controllable objects and metrics to support most typical transportation system optimization scenarios. These controllable objects and metrics are all provided by Python API for ease of use. We choose five important and representative transportation system optimization scenarios and benchmark classical rule-based algorithms, reinforcement learning, and black-box optimization in four cities. The codes are available at \url{https://github.com/tsinghua-fib-lab/moss-benchmark} with the MIT License.

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