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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    Title :

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


    Contributors:
    Ye, Peijun (author) / Zhu, Fenghua (author) / Lv, Yisheng (author) / Wang, Xiao (author) / Chen, Yuanyuan (author)


    Publication date :

    2022-10-08


    Size :

    1970581 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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