A parallel computing framework for large-scale microscopic traffic simulation based on spectral partitioning

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A parallel computing framework for large-scale microscopic traffic simulation based on spectral partitioning

Liu, Zhiyuan / Xie, Shen / Zhang, Honggang / Zhou, Dinghao / Yang, Yuwei

Highlights • A novel parallel computing framework is developed for large-scale microscopic traffic simulation. • This framework considers vehicle information and road information as constitutive components for node weight determination. • An improved spectral partitioning method is proposed to modify the partitioning outcomes, enhancing simulation efficiency. • The framework's validity is demonstrated through experiments conducted on road networks of diverse scales and densities.

Abstract This paper introduces a parallel computing framework based on the Spectral Partitioning (SP) method designed to enhance the computational efficiency of large-scale microscopic traffic simulation (LSMTS). The framework employs the SP method to partition road networks, taking into account vehicle information and road information as constitutive components for node weight determination. Micro-simulation relies on vehicle information from both preceding and following vehicles to accurately infer the operational states of a vehicle. However, network partitioning can disrupt the flow of vehicle information, resulting in its loss. To address this, the proposed framework incorporates a boundary transmission method to ensure simulation accuracy and precision. This study presents an improved SP (iSP) method tailored for LSMTS, further enhancing the partitioning results achieved through the SP method. Lastly, the framework's validity is confirmed through road network experiments of varying scales and densities, with comparisons made to existing parallel simulation methods. The results demonstrate that the framework significantly reduces the execution time of simulation tasks while maintaining a high level of load balance and minimizing communication overhead.