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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    Title :

    Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks


    Contributors:
    Zhang, Chao (author) / Li, Yechen (author) / Arora, Neha (author) / Pierce, Damien (author) / Osorio, Carolina (author)


    Publication date :

    2024-09-24


    Size :

    433088 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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