Multi-Directional Flow as Touch-Stone to Assess Models of Pedestrian Dynamics

TL;DR

This paper introduces a multi-directional flow scenario as a sensitive test for calibrating and validating pedestrian dynamics models, demonstrating high sensitivity to parameter changes and differences between models, with potential for simplified evaluation.

Contribution

It proposes a novel multi-directional flow scenario with evaluation measures that effectively detect parameter and model differences in pedestrian dynamics simulations.

Findings

Evaluation measures are highly sensitive to parameter changes.

Symmetry breaking in simulations amplifies differences between models.

Scenario can summarize complex dynamics in a single numerical value.

Abstract

For simulation models of pedestrian dynamics there are always the issues of calibration and validation. These are usually done by comparing measured properties of the dynamics found in observation, experiments and simulation in certain scenarios. For this the scenarios first need to be sensitive to parameter changes of a particular model or - if models are compared - differences between models. Second it is helpful if the exhibited differences can be expressed in quantities which are as simple as possible ideally a single number. Such a scenario is proposed in this contribution together with evaluation measures. In an example evaluation of a particular model it is shown that the proposed evaluation measures are very sensitive to parameter changes and therefore summarize differences effects of parameter changes and differences between models efficiently, sometimes in a single number. It is shown how the symmetry which exists in the achiral geometry of the proposed example scenario is broken in particular simulation runs exhibiting chiral dynamics, while in the statistics of 1,000 simulation runs there is a symmetry between left- and right-chiral dynamics. In the course of the symmetry breaking differences between models and parameter settings are amplified which is the origin of the high sensitivity of the scenario against parameter changes.