In agent-based traffic simulation, calibration is an essential stage before the models applied to reproduce the individual/group travel behaviors. While traditional methods suffer from a high computational complexity, this paper proposes an improved method to alleviate the computational burden for large-scaled simulations. Specifically, we introduce variational auto-encoder to compress the original agent state vector into a lower dimensional hidden space, where the state transfer probability is calculated fast. Then the probability is mapped into the original space through a decoder, to achieve the agent travel parameters. The dynamic calibration method is tested with other baselines in urban travel demand analysis. Experiment results demonstrate that our method brings about 19% elevation of efficiency with the same accuracy of calibration.


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    Titel :

    Efficient Calibration of Agent-Based Traffic Simulation Using Variational Auto-Encoder


    Beteiligte:
    Ye, Peijun (Autor:in) / Zhu, Fenghua (Autor:in) / Lv, Yisheng (Autor:in) / Wang, Xiao (Autor:in) / Chen, Yuanyuan (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    1970581 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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