Highlights Algorithm for high-dimensional OD matrix calibration for large-scale networks. High-dimensional problem with 4050 decision variables for a large-scale Singapore network. Improves the objective function by two orders of magnitude within 2 simulation runs. Outperforms a benchmark method by 2 orders of magnitude.

    Abstract This paper considers high-dimensional offline calibration problems for large-scale simulation-based network models. We propose a metamodel simulation-based optimization (SO) approach. The proposed method is formulated and validated on a simple synthetic toy network. It is then applied to a high-dimensional case study of a large-scale Singapore network. Compared to two benchmark methods, a derivative-free pattern search method and the SPSA method, the proposed method improves the objective function estimates by two orders of magnitude. Moreover, this improvement is achieved after only 2 simulation runs. Hence, the proposed method is computationally efficient. The main idea of the proposed approach is to embed, within the SO algorithm, information from an analytical (i.e., lower-resolution) yet differentiable and tractable network model. It is this analytical structural information that enables the SO algorithm to become both suitable for high-dimensional problems and computationally efficient. For a network with n links, the analytical network model is implemented as a system of n nonlinear equations. Hence, it scales linearly with the number of links in the network and independently of link attributes (such as link length) and of the dimension of the route choice set.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks


    Beteiligte:


    Erscheinungsdatum :

    2019-01-15


    Format / Umfang :

    26 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Efficient calibration techniques for large-scale traffic simulators

    Zhang, Chao / Osorio, Carolina / Flötteröd, Gunnar | Elsevier | 2016


    A novel metamodel-based framework for large-scale dynamic origin–destination demand calibration

    Dantsuji, Takao / Hoang, Nam H. / Zheng, Nan et al. | Elsevier | 2021


    Study on Dynamic Origin-Destination Estimation of Large-Scale Road Networks

    Li, Jun-wei / Lu, Hua-pu / Geng, Xue-fei et al. | ASCE | 2011