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Assessment of models for pedestrian dynamics with functional principal component analysis

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  • Chraibi, Mohcine
  • Ensslen, Tim
  • Gottschalk, Hanno
  • Saadi, Mohamed
  • Seyfried, Armin

Abstract

Many agent based simulation approaches have been proposed for pedestrian flow. As such models are applied e.g. in evacuation studies, the quality and reliability of such models is of vital interest. Pedestrian trajectories are functional data and thus functional principal component analysis is a natural tool to assess the quality of pedestrian flow models beyond average properties. In this article we conduct functional Principal Component Analysis (PCA) for the trajectories of pedestrians passing through a bottleneck. In this way it is possible to assess the quality of the models not only on basis of average values but also by considering its fluctuations. We benchmark two agent based models of pedestrian flow against the experimental data using PCA average and stochastic features. Functional PCA proves to be an efficient tool to detect deviation between simulation and experiment and to assess quality of pedestrian models.

Suggested Citation

  • Chraibi, Mohcine & Ensslen, Tim & Gottschalk, Hanno & Saadi, Mohamed & Seyfried, Armin, 2016. "Assessment of models for pedestrian dynamics with functional principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 475-489.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:475-489
    DOI: 10.1016/j.physa.2016.01.058
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    References listed on IDEAS

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    1. Anders Johansson & Dirk Helbing & Pradyumn K. Shukla, 2007. "Specification Of The Social Force Pedestrian Model By Evolutionary Adjustment To Video Tracking Data," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(supp0), pages 271-288.
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    3. Nishinari, Katsuhiro & Sugawara, Ken & Kazama, Toshiya & Schadschneider, Andreas & Chowdhury, Debashish, 2006. "Modelling of self-driven particles: Foraging ants and pedestrians," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(1), pages 132-141.
    4. Shiwakoti, Nirajan & Sarvi, Majid & Rose, Geoff & Burd, Martin, 2011. "Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1433-1449.
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    Cited by:

    1. Bode, Nikolai W.F. & Chraibi, Mohcine & Holl, Stefan, 2019. "The emergence of macroscopic interactions between intersecting pedestrian streams," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 197-210.

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