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  • Forschungszentrum Julich, Zentralbibliothek  (2)
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  • Forschungszentrum Julich, Zentralbibliothek  (2)
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  • 1
    Online Resource
    Online Resource
    Forschungszentrum Julich, Zentralbibliothek ; 2019
    In:  Collective Dynamics Vol. 4 ( 2019-09-03)
    In: Collective Dynamics, Forschungszentrum Julich, Zentralbibliothek, Vol. 4 ( 2019-09-03)
    Abstract: Pedestrian dynamics is an interdisciplinary field of research. Psychologists, sociologists, traffic engineers, physicists, mathematicians and computer scientists all strive to understand the dynamics of a moving crowd. In principle, computer simulations offer means to further this understanding. Yet, unlike for many classic dynamical systems in physics, there is no universally accepted locomotion model for crowd dynamics. On the contrary, a multitude of approaches, with very different characteristics, compete. Often only the experts in one special model type are able to assess the consequences these characteristics have on a simulation study. Therefore, scientists from all disciplines who wish to use simulations to analyze pedestrian dynamics need a tool to compare competing approaches. Developers, too, would profit from an easy way to get insight into an alternative modeling ansatz. Vadere meets this interdisciplinary demand by offering an open-source simulation framework that is lightweight in its approach and in its user interface while offering pre-implemented versions of the most widely spread models.
    Type of Medium: Online Resource
    ISSN: 2366-8539
    Language: Unknown
    Publisher: Forschungszentrum Julich, Zentralbibliothek
    Publication Date: 2019
    detail.hit.zdb_id: 2854776-7
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  • 2
    Online Resource
    Online Resource
    Forschungszentrum Julich, Zentralbibliothek ; 2020
    In:  Collective Dynamics Vol. 5 ( 2020-03-27)
    In: Collective Dynamics, Forschungszentrum Julich, Zentralbibliothek, Vol. 5 ( 2020-03-27)
    Abstract: In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We use density heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.
    Type of Medium: Online Resource
    ISSN: 2366-8539
    Language: Unknown
    Publisher: Forschungszentrum Julich, Zentralbibliothek
    Publication Date: 2020
    detail.hit.zdb_id: 2854776-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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