A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking
Keywords: microscopic traffic simulator, transportation system optimization, GPU acceleration
TL;DR: The first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation and optimization.
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 and optimization methods.
Learning-based optimization methods require extensive interactions with highly realistic microscopic traffic simulators.
However, existing microscopic traffic simulators are inefficient in large-scale scenarios and thus fail to support the adoption of these methods in large-scale transportation system optimization scenarios.
In addition, the optimization scenarios supported by existing simulators are limited, mainly focusing on the traffic signal control.
To address these challenges, 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 compared to the best baseline CityFlow.
Besides, it achieves a more realistic average road speeds simulated on real datasets by adopting the IDM model as the car-following model and the randomized MOBIL model as the lane-changing model.
Based on it, we implement a set of microscopic and macroscopic controllable objects and metrics provided by Python API to support typical transportation system optimization scenarios including traffic signal control, dynamic lane assignment within junctions, tidal lane control, congestion pricing, road planning, e.t.c.
We choose five representative transportation system optimization scenarios and benchmark classical rule-based algorithms, reinforcement learning algorithms, and black-box optimization algorithms in four cities.
These experiments effectively demonstrate the usability of the simulator for large-scale traffic system optimization.
The anonymous code of the simulator is available at https://anonymous.4open.science/r/moss-AF45 and the others are shown at Appendix A.
In addition, we build an open-registration web platform to support no-code trials.
Primary Area: infrastructure, software libraries, hardware, systems, etc.
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Submission Number: 7274
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