2017
Alessandro Corbetta; Chung-min Lee; Roberto Benzi; Adrian Muntean; Federico Toschi
Fluctuations around mean walking behaviours in diluted pedestrian flows Journal Article
In: Physical Review E, vol. 95, pp. 032316, 2017.
Abstract | Links | BibTeX | Tags: High statistic measurements, ILIAD15_STA, Langevin Equations, Metaforum, Pedestrian dynamics
@article{Corbetta_PRE_2017,
title = {Fluctuations around mean walking behaviours in diluted pedestrian flows},
author = {Alessandro Corbetta and Chung-min Lee and Roberto Benzi and Adrian Muntean and Federico Toschi},
url = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.032316},
year = {2017},
date = {2017-03-15},
journal = {Physical Review E},
volume = {95},
pages = {032316},
abstract = {Understanding and modeling the dynamics of pedestrian crowds can help with designing and increasing the safety of civil facilities. A key feature of a crowd is its intrinsic stochasticity, appearing even under very diluted conditions, due to the variability in individual behaviors. Individual stochasticity becomes even more important under densely crowded conditions, since it can be nonlinearly magnified and may lead to potentially dangerous collective behaviors. To understand quantitatively crowd stochasticity, we study the real-life dynamics of a large ensemble of pedestrians walking undisturbed, and we perform a statistical analysis of the fully resolved pedestrian trajectories obtained by a yearlong high-resolution measurement campaign. Our measurements have been carried out in a corridor of the Eindhoven University of Technology via a combination of Microsoft Kinect 3D range sensor and automatic head-tracking algorithms. The temporal homogeneity of our large database of trajectories allows us to robustly define and separate average walking behaviors from fluctuations parallel and orthogonal with respect to the average walking path. Fluctuations include rare events when individuals suddenly change their minds and invert their walking directions. Such tendency to invert direction has been poorly studied so far, even if it may have important implications on the functioning and safety of facilities. We propose a model for the dynamics of undisturbed pedestrians, based on stochastic differential equations, that provides a good agreement with our field observations, including the occurrence of rare events.},
keywords = {High statistic measurements, ILIAD15_STA, Langevin Equations, Metaforum, Pedestrian dynamics},
pubstate = {published},
tppubtype = {article}
}
Understanding and modeling the dynamics of pedestrian crowds can help with designing and increasing the safety of civil facilities. A key feature of a crowd is its intrinsic stochasticity, appearing even under very diluted conditions, due to the variability in individual behaviors. Individual stochasticity becomes even more important under densely crowded conditions, since it can be nonlinearly magnified and may lead to potentially dangerous collective behaviors. To understand quantitatively crowd stochasticity, we study the real-life dynamics of a large ensemble of pedestrians walking undisturbed, and we perform a statistical analysis of the fully resolved pedestrian trajectories obtained by a yearlong high-resolution measurement campaign. Our measurements have been carried out in a corridor of the Eindhoven University of Technology via a combination of Microsoft Kinect 3D range sensor and automatic head-tracking algorithms. The temporal homogeneity of our large database of trajectories allows us to robustly define and separate average walking behaviors from fluctuations parallel and orthogonal with respect to the average walking path. Fluctuations include rare events when individuals suddenly change their minds and invert their walking directions. Such tendency to invert direction has been poorly studied so far, even if it may have important implications on the functioning and safety of facilities. We propose a model for the dynamics of undisturbed pedestrians, based on stochastic differential equations, that provides a good agreement with our field observations, including the occurrence of rare events.
The processing scripts for our datasets can be found on github .