Automated Quality Assessment of Space-Continuous Models for Pedestrian Dynamics

TL;DR

This paper introduces an automated method to evaluate and optimize space-continuous pedestrian models by comparing simulated data with empirical data using a Kolmogorov-Smirnov based evaluation factor.

Contribution

It presents a novel automated assessment framework for pedestrian models that quantifies goodness-of-fit and optimizes model parameters.

Findings

Effective evaluation factor for model assessment

Automated parameter optimization process

Quantitative comparison between simulated and empirical data

Abstract

In this work we propose a methodology for assessment of pedestrian models continuous in space. With respect to the Kolmogorov-Smirnov distance between two data clouds, representing for instance simulated and the corresponding empirical data, we calculate an evaluation factor between zero and one. Based on the value of the herein developed factor, we make a statement about the goodness of the model under evaluation. Moreover this process can be repeated in an automatic way in order to maximize the above mentioned factor and hence determine the optimal set of model parameters.