Publications

@phdthesis{Corbetta_PDE16,
title = {Multiscale crowd dynamics: physical analysis, modeling and applications},
author = {Alessandro Corbetta},
url = {http://repository.tue.nl/812292},
doi = {978-90-386-4014-3},
year = {2016},
date = {2016-02-02},
school = {Eindhoven University of Technology},
abstract = {In this thesis we investigate the dynamics of pedestrian crowds in a fundamental and applied perspective. Envisioning a quantitative understanding we employ ad hoc largescale
experimental measurements as well as analytic and numerical models. Moreover, we analyze current regulations in matter of pedestrians structural actions (structural loads),
in view of the need of guaranteeing pedestrian safety in serviceable built environments.
This work comes in three complementary parts, in which we adopt distinct perspectives
and conceptually different tools, respectively from statistical physics, mathematical modeling and structural engineering.

The statistical dynamics of individual pedestrians is the subject of the first part of this thesis. Although individual trajectories may appear random, once we analyze them in large ensembles we expect “preferred” behaviors to emerge. Thus, we envisage individual paths as fluctuations around such established routes. To investigate this aspect, we perform year-long 24/7 measurements of pedestrian trajectories in real-life conditions, which we analyze statistically and via Langevin-like models. Two measurement locations have been considered: a corridor-shaped landing in the Metaforum building at Eindhoven University of Technology and the main walkway within Eindhoven Train Station. The measurement technique we employ is based on overhead Microsoft Kinect™ 3D-range sensors and on ad hoc tracking algorithms.

In the second part of the thesis, we zoom out from the perspective of individual pedestrians and we look at crowds, adopting a genuine mathematical modeling point of view. We establish a general background of crowd dynamics modeling, which includes an introduction to the modeling framework by Cristiani, Piccoli and Tosin (CPT). This framework is suitable to model systems governed by social interactions and stands on a first order measure-valued evolution equation. Measures enable a unified treatment of crowd flows at the microscopic (particle-like) and macroscopic (fluid-like) observation scales. In a Wasserstein space context, we wonder when the microscopic and macroscopic dynamics are consistent as the number of agents involved grows. In this comparison we consider agents whose mass (in a measure sense) is independent on the size of the crowd. Then, we focus on the modeling of crowds moving across footbridge-like (i.e. elongated) geometries. In these simple scenarios we are able to give a reasonable form to the CPT model components from phenomenological considerations and thus perform simulations.

In the third part of the thesis, we consider crowd flows on footbridges in relation to the way the safety of pedestrians is ensured by the current building practice. We address crowd-footbridge systems in terms of featured uncertainties. We provide a review of the literature giving a synthetic comparison of uncertainties involved. In general, beside the uncertainties affecting the mechanical properties of the structure, the status of the crowd is itself uncertain. Taking inspiration from wind engineering, we approach the crowd dynamics through a distinction between the approaching traffic and the crossing traffic.
In the review, we consider how building regulations address the crowd load. On one hand, no uncertainty, nor variability, is considered on the crowd state, therefore the roughest possible model (constant load) is typically retained. On the other hand, we notice how a large dissent is present in the prescribed load values, suggesting a possible inadequacy in regulations. Finally, we propose a framework to deal with uncertainties related to the crowd traffic, and specifically the crowd density. },
keywords = {Eindhoven Station, High statistic measurements, Metaforum},
pubstate = {published},
tppubtype = {phdthesis}
}