Assessment of models for pedestrian dynamics with functional principal component analysis

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

Physica A: Statistical Mechanics and its Applications, 2016, vol. 451, issue C, 475-489

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.

Keywords: Pedestrian dynamics; Statistical analysis; Comparison with experiment; Functional PCA; Model quality (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:451:y:2016:i:c:p:475-489

DOI: 10.1016/j.physa.2016.01.058

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