This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.


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    Titel :

    Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks


    Beteiligte:
    Zhang, Chao (Autor:in) / Li, Yechen (Autor:in) / Arora, Neha (Autor:in) / Pierce, Damien (Autor:in) / Osorio, Carolina (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    433088 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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