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2021 | Book

Behavioral Human Crowds: Recent Results and New Research Frontiers Read chapter Read first chapter

Crowd Dynamics, Volume 3

Modeling and Social Applications in the Time of COVID-19

Editors: Nicola Bellomo, Livio Gibelli

Publisher: Springer International Publishing

Book Series : Modeling and Simulation in Science, Engineering and Technology

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This contributed volume explores innovative research in the modeling, simulation, and control of crowd dynamics. Chapter authors approach the topic from the perspectives of mathematics, physics, engineering, and psychology, providing a comprehensive overview of the work carried out in this challenging interdisciplinary research field. In light of the recent COVID-19 pandemic, special consideration is given to applications of crowd dynamics to the prevention of the spreading of contagious diseases. Some of the specific topics covered in this volume include:
- Impact of physical distancing on the evacuation of crowds- Generalized solutions of opinion dynamics models- Crowd dynamics coupled with models for infectious disease spreading- Optimized strategies for leaders in controlling the dynamics of a crowd
Crowd Dynamics, Volume 3 is ideal for mathematicians, engineers, physicists, and other researchers working in the rapidly growing field of modeling and simulation of human crowds.

Table of Contents

Frontmatter
Behavioral Human Crowds: Recent Results and New Research Frontiers
Abstract
This editorial chapter provides an introduction to the contents of this edited book and a general critical analysis which looks ahead to research perspectives. The presentation is organized in three parts. In the first part some key research topics are selected based not only on their theoretical interest but also on the potential impact that may have on the society well-being. The second part outlines the contents of the following chapters in light of the aforementioned key topics as well as of the preceding edited books (N. Bellomo and L. Gibelli, Crowd Dynamics, Volume 1 - Theory, Models, and Safety Problems, Birkhäuser, New York, 2018; L. Gibelli, Crowd Dynamics, Volume 2 - Theory, Models, and Applications, Birkhäuser, New York, 2020). The last part speculates on promising future research directions.
Nicola Bellomo, Livio Gibelli
Generalized Solutions to Opinion Dynamics Models with Discontinuities
Abstract
Social dynamics models may present discontinuities in the right-hand side of the dynamics for multiple reasons, including topology changes and quantization. Several concepts of generalized solutions for discontinuous equations are available in the literature and are useful to analyze these models. In this chapter, we study Caratheodory and Krasovsky generalized solutions for discontinuous models of opinion dynamics with state dependent interactions. We consider two definitions of “bounded confidence” interactions, which we, respectively, call metric and topological: in the former, individuals interact if their opinions are closer than a threshold; in the latter, individuals interact with a fixed number of nearest neighbors. We compare the dynamics produced by the two kinds of interactions in terms of existence, uniqueness, and asymptotic behavior of different types of solutions.
Francesca Ceragioli, Paolo Frasca, Benedetto Piccoli, Francesco Rossi
Crowd Behaviour Understanding Using Computer Vision and Statistical Mechanics Principles
Abstract
Crowd behaviour understanding in computer science is a research discipline which has grown rapidly in recent years. Specifically, we are currently able to generate large and high-resolution observation data through crowd sensing in varieties of spatial environments. This has also given us the advantage to adopt computer vision methods for detecting human behaviour. In this study, we adopted statistical mechanics principles with analogies of entropy and kinetic energy in classical molecular gases to derive features which describe crowd motions. These are implicitly measured, as basis for understanding behaviour, using a holistic three-dimensional representation, of crowd features including structure, energy and translation. As a result, we measured those features using computer vision in the view of machine understanding crowd behaviour. Usual behaviour is established from our expected crowd motions in context of the specific recipient spaces of our experiments. The behaviour which does not fall within the expected usual behaviour measurement is considered as an unusual behaviour. This research work was initiated in 2013 under the eVACUATE project, while it is currently ongoing under the S4AllCities project since 2020.
Zoheir Sabeur, Banafshe Arbab-Zavar
Applications of Crowd Dynamic Models: Feature Analysis and Process Optimization
Abstract
Recently, researchers studying crowd dynamics models have attempted to build a universal system for informing pedestrian traffic management. To achieve this goal, it is necessary to improve the research conducted on specific and detailed problems. In this chapter, we review our research work concerning two problems related to evacuation management: features analysis and process optimization. By summarizing previous studies and relevant literature, we will discuss the application of crowd dynamics models for solving the above-mentioned problems.
Liang Li, Hong Liu, Yanbin Han, Guijuan Zhang, Dianjie Lu
Optimized Leaders Strategies for Crowd Evacuation in Unknown Environments with Multiple Exits
Abstract
In this chapter, we discuss the mathematical modeling of egressing pedestrians in an unknown environment with multiple exits. We investigate different control problems to enhance the evacuation time of a crowd of agents, by few informed individuals, named leaders. Leaders are not recognizable as such and consist of two groups: a set of unaware leaders moving selfishly toward a fixed target, whereas the rest is coordinated to improve the evacuation time introducing different performance measures. Follower-leader dynamics is initially described microscopically by an agent-based model, subsequently a mean-field type model is introduced to approximate the large crowd of followers. The mesoscopic scale is efficiently solved by a class of numerical schemes based on direct simulation Monte-Carlo methods. Optimization of leader strategies is performed by a modified compass search method in the spirit of metaheuristic approaches. Finally, several virtual experiments are studied for various control settings and environments.
Giacomo Albi, Federica Ferrarese, Chiara Segala
The Impact of Physical Distancing on the Evacuation of Crowds
Abstract
One of the key implications of COVID-19 is the adoption of physical distancing provisions to minimise the risk of virus transmission. Physical distancing can have significant consequences on crowd movement both in normal conditions and during emergencies. The impact of physical distancing is discussed in this chapter by first presenting an overview of its implications on crowd dynamics and space usage. This is followed by an assessment of expected changes in crowd behaviour, including changes in the fundamental walking speed/density and flow/density relationships. Findings from an experiment investigating the impact of physical distancing on flow rates through doors are presented. In addition, a set of recommendations concerning modifications of the hand calculations currently used for evacuation design (e.g. hydraulic models) are presented alongside a discussion on possible modifications to agent-based crowd models. A verification test to evaluate the results produced by crowd evacuation modelling tools considering physical distancing is also presented. This chapter highlights the importance of considering the increased movement time due to physical distancing in evacuation design and provides insights on how to account for this issue in crowd modelling.
Enrico Ronchi, Daniel Nilsson, Ruggiero Lovreglio, Mikayla Register, Kyla Marshall
A Kinetic Theory Approach to Model Crowd Dynamics with Disease Contagion
Abstract
We present some ideas on how to extend a kinetic-type model for crowd dynamics to account for an infectious disease spreading. We focus on a medium size crowd occupying a confined environment where the disease is easily spread. The kinetic theory approach we choose uses tools of game theory to model the interactions of a person with the surrounding people and the environment, and it features a parameter to represent the level of stress. It is known that people choose different walking strategies when subjected to fear or stressful situations. To demonstrate that our model for crowd dynamics could be used to reproduce realistic scenarios, we simulate passengers in one terminal of Hobby Airport in Houston. In order to model disease spreading in a walking crowd, we introduce a variable that denotes the level of exposure to people spreading the disease. In addition, we introduce a parameter that describes the contagion interaction strength and a kernel function that is a decreasing function of the distance between a person and a spreading individual. We test our contagion model on a problem involving a small crowd walking through a corridor.
Daewa Kim, Annalisa Quaini
Toward a Quantitative Reduction of the SIR Epidemiological Model
Abstract
Motivated by our intention to use SIR-type epidemiological models in the context of dynamic networks, we investigate in this framework possibilities to reduce the classical SIR model to a representative evolution model for a suitably chosen observable. For selected scenarios, we provide practical a priori error bounds between the approximate and the original observables. Finally, we illustrate numerically the behavior of the reduced models compared to the original ones. As a long-term goal, we would like to apply such techniques in the context of large-scale highly interacting inhomogeneous human crowds.
Matteo Colangeli, Adrian Muntean
An Agent-Based Model of COVID-19 Diffusion to Plan and Evaluate Intervention Policies
Abstract
A model of interacting agents, following plausible behavioral rules into a world where the Covid-19 epidemic is affecting the actions of everyone. The model works with (i) infected agents categorized as symptomatic or asymptomatic and (ii) the places of contagion specified in a detailed way. The infection transmission is related to three factors: the characteristics of both the infected person and the susceptible one, plus those of the space in which contact occurs. The model includes the structural data of Piedmont, an Italian region, but we can easily calibrate it for other areas. The micro-based structure of the model allows factual, counterfactual, and conditional simulations to investigate both the spontaneous or controlled development of the epidemic.
The model is generative of complex epidemic dynamics emerging from the consequences of agents’ actions and interactions, with high variability in outcomes and stunning realistic reproduction of the successive contagion waves in the reference region. There is also an inverse generative side of the model, coming from the idea of using genetic algorithms to construct a meta-agent to optimize the vaccine distribution. This agent takes into account groups’ characteristics—by age, fragility, work conditions—to minimize the number of symptomatic people.
Gianpiero Pescarmona, Pietro Terna, Alberto Acquadro, Paolo Pescarmona, Giuseppe Russo, Emilio Sulis, Stefano Terna
Metadata
Title
Crowd Dynamics, Volume 3
Editors
Nicola Bellomo
Livio Gibelli
Copyright Year
2021
Electronic ISBN
978-3-030-91646-6
Print ISBN
978-3-030-91645-9
DOI
https://doi.org/10.1007/978-3-030-91646-6

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