Data-driven simulationMartin, Rafael F.Parisi, Daniel R.Data-driven simulation of pedestrian dynamicsPedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networksGeneralized Regression Neural Network (GRNN), which does not have to deal with a huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian—one obstacle problem. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction.


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    Titel :

    Data-Driven Simulation for Pedestrians Avoiding a Fixed Obstacle


    Weitere Titelangaben:

    Springer Proceedings Phys.


    Beteiligte:
    Zuriguel, Iker (Herausgeber:in) / Garcimartin, Angel (Herausgeber:in) / Cruz, Raul (Herausgeber:in) / Martin, Rafael F. (Autor:in) / Parisi, Daniel R. (Autor:in)

    Erschienen in:

    Traffic and Granular Flow 2019 ; Kapitel : 25 ; 205-210


    Erscheinungsdatum :

    2020-11-17


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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