Enhanced empirical data for the fundamental diagram and the flow through bottlenecks

TL;DR

This paper provides a comprehensive review and new experimental data on pedestrian flow, focusing on the fundamental diagram and bottleneck flow, highlighting measurement method influences and improving validation of pedestrian models.

Contribution

It offers new high-precision experimental data and analyzes how measurement methods affect the fundamental diagram and bottleneck flow in pedestrian dynamics.

Findings

Large deviations found among different measurement methods

Measurement method influences the fundamental diagram significantly

Experimental data helps validate pedestrian flow models

Abstract

In recent years, several approaches for modelling pedestrian dynamics have been proposed and applied e.g. for design of egress routes. However, so far not much attention has been paid to their 'quantitative' validation. This unsatisfactory situation belongs amongst others on the uncertain and contradictory experimental data base. The fundamental diagram, i.e. the density-dependence of the flow or velocity, is probably the most important relation as it connects the basic parameter to describe the dynamic of crowds. But specifications in different handbooks as well as experimental measurements differ considerably. The same is true for the bottleneck flow. After a comprehensive review of the experimental data base we give an survey of a research project, including experiments with up to 250 persons performed under well controlled laboratory conditions. The trajectories of each person are measured in high precision to analyze the fundamental diagram and the flow through bottlenecks. The trajectories allow to study how the way of measurement influences the resulting relations. Surprisingly we found large deviation amongst the methods. These may be responsible for the deviation in the literature mentioned above. The results are of particular importance for the comparison of experimental data gained in different contexts and for the validation of models.