publications

@article{0c6a159182504960bcab1be66495fb3c,
title = {Pedestrian orientation dynamics from high-fidelity measurements},
author = {Joris Willems and Alessandro Corbetta and Vlado Menkovski and Federico Toschi},
year = {2020},
date = {2020-01-01},
journal = {arXiv},
publisher = {Cornell University Library},
abstract = {We investigate in real-life conditions and with very high accuracy the dynamics of body rotation, or yawing, of walking pedestrians - an highly complex task due to the wide variety in shapes, postures and walking gestures. We propose a novel measurement method based on a deep neural architecture that we train on the basis of generic physical properties of the motion of pedestrians. Specifically, we leverage on the strong statistical correlation between individual velocity and body orientation: the velocity direction is typically orthogonal with respect to the shoulder line. We make the reasonable assumption that this approximation, although instantaneously slightly imperfect, is correct on average. This enables us to use velocity data as training labels for a highly-accurate point-estimator of individual orientation, that we can train with no dedicated annotation labor. We discuss the measurement accuracy and show the error scaling, both on synthetic and real-life data: we show that our method is capable of estimating orientation with an error as low as 7.5 degrees. This tool opens up new possibilities in the studies of human crowd dynamics where orientation is key. By analyzing the dynamics of body rotation in real-life conditions, we show that the instantaneous velocity direction can be described by the combination of orientation and a random delay, where randomness is provided by an Ornstein-Uhlenbeck process centered on an average delay of 100ms. Quantifying these dynamics could have only been possible thanks to a tool as precise as that proposed.},
keywords = {cs.LG, physics.soc-ph},
pubstate = {published},
tppubtype = {article}
}

@article{ba1dbdb0ddd24f03bd2313cde67ca761,
title = {Fluctuations around mean walking behaviours in diluted pedestrian flows},
author = {A Corbetta and C -M Lee and R Benzi and A Muntean and F Toschi},
year = {2016},
date = {2016-01-01},
journal = {arXiv.org, e-Print Archive, Physics},
number = {1610.07429v1},
pages = {1--10},
abstract = {Understanding and modeling the dynamics of pedestrian crowds can help with designing and increasing the safety of civil facilities. A key feature of crowds is its intrinsic stochasticity, appearing even under very diluted conditions, due to the variability in individual behaviours. Individual stochasticity becomes even more important under densely crowded conditions, since it can be nonlinearly magnified and may lead to potentially dangerous collective behaviours. 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 year-long 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 behaviours 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 direction. 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 novel model for the dynamics of undisturbed pedestrians, based on stochastic differential equations, that provides a good agreement with our experimental observations, including the occurrence of rare events.},
keywords = {physics.data-an, physics.soc-ph},
pubstate = {published},
tppubtype = {article}
}

@article{68dcea056c30443eaa8e4cafce542089,
title = {Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway},
author = {A Corbetta and J A Meeusen and C -M Lee and F Toschi},
year = {2016},
date = {2016-01-01},
journal = {arXiv},
pages = {1--9},
publisher = {Cornell University Library},
abstract = {Employing partially overlapping overhead kinectTMS sensors and automatic pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear section of the main walkway of Eindhoven train station on a 24/7 basis. Beside giving access to the train platforms (it passes underneath the railways), the walkway plays an important connection role in the city. Several crowding scenarios occur during the day, including high- and low-density dynamics in uni- and bi-directional regimes. In this paper we discuss our recording technique and we illustrate preliminary data analyses. Via fundamental diagrams-like representations we report pedestrian velocities and fluxes vs. pedestrian density. Considering the density range $0$ - $1.1,$ped/m$^2$, we find that at densities lower than $0.8,$ped/m$^2$ pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above $0.8,$ped/m$^2$.},
note = {9 pages, 7 figures},
keywords = {physics.data-an, physics.soc-ph},
pubstate = {published},
tppubtype = {article}
}