High statistics measurements of pedestrian dynamics

TL;DR

This paper presents extensive high-accuracy measurements of pedestrian trajectories in a real-world setting, providing detailed statistical descriptions of pedestrian movement and crowd behavior to improve models for safety and comfort.

Contribution

It introduces a large dataset of over 100,000 pedestrian trajectories collected with Kinect technology, enabling detailed analysis of pedestrian dynamics and crowd conditions.

Findings

Statistical descriptions of pedestrian positions and velocities.

Analysis of fundamental diagrams conditioned on crowding.

Insights into pedestrian flow behavior in different crowding scenarios.

Abstract

Understanding the complex behavior of pedestrians walking in crowds is a challenge for both science and technology. In particular, obtaining reliable models for crowd dynamics, capable of exhibiting qualitatively and quantitatively the observed emergent features of pedestrian flows, may have a remarkable impact for matters as security, comfort and structural serviceability. Aiming at a quantitative understanding of basic aspects of pedestrian dynamics, extensive and high-accuracy measurements of pedestrian trajectories have been performed. More than 100.000 real-life, time-resolved trajectories of people walking along a trafficked corridor in a building of the Eindhoven University of Technology, The Netherlands, have been recorded. A measurement strategy based on Microsoft Kinect\texttrademark has been used; the trajectories of pedestrians have been analyzed as ensemble data. The main result consists of a statistical descriptions of pedestrian characteristic kinematic quantities such as positions and fundamental diagrams, possibly conditioned to local crowding status (e.g., one or more pedestrian(s) walking, presence of co-flows and counter-flows).