Assessment of Models for Pedestrian Dynamics with Functional Principal Component Analysis

TL;DR

This paper evaluates pedestrian flow models using functional principal component analysis on trajectories, demonstrating PCA's effectiveness in detecting deviations from experimental data and assessing model quality.

Contribution

It introduces the application of functional PCA to compare and benchmark pedestrian flow models against real trajectories, highlighting its utility beyond average properties.

Findings

Functional PCA effectively detects deviations between models and experimental data.

PCA captures stochastic features of pedestrian trajectories.

The method provides a reliable assessment of model quality.

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 asses the quality of pedestrian flow models beyond average properties. In this article we conduct functional PCA for the trajectories of pedestrians passing through a bottleneck. 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 asses quality of pedestrian models.