Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation

TL;DR

The paper introduces LPSim, a GPU-accelerated traffic simulation framework that significantly reduces computation time for large-scale urban traffic modeling, enabling faster and more detailed analysis.

Contribution

LPSim is a novel multi-GPU based simulation framework that achieves high scalability and efficiency for large-scale traffic networks, surpassing traditional CPU methods.

Findings

Simulates 2.82 million trips in 6.28 minutes on a single GPU.

Achieves 113 times faster performance than CPU-based methods on dual GPUs.

Successfully simulates 9.01 million trips in 21.16 minutes on dual GPUs.

Abstract

Traffic propagation simulation is crucial for urban planning, enabling congestion analysis, travel time estimation, and route optimization. Traditional micro-simulation frameworks are limited to main roads due to the complexity of urban mobility and large-scale data. We introduce the Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), a scalable tool that leverages GPU parallel computing to simulate extensive traffic networks with high fidelity and reduced computation time. LPSim performs millions of vehicle dynamics simulations simultaneously, outperforming CPU-based methods. It can complete simulations of 2.82 million trips in 6.28 minutes using a single GPU, and 9.01 million trips in 21.16 minutes on dual GPUs. LPSim is also tested on dual NVIDIA A100 GPUs, achieving simulations about 113 times faster than traditional CPU methods. This demonstrates its scalability and efficiency for large-scale applications, making LPSim a valuable resource for researchers and planners. Code: https://github.com/Xuan-1998/LPSim