Modeling Information Transfer in High-Density Crowds

U.U.D.M. Project Report 2018:24 Modeling Information Transfer in High-Density Crowds Olle Eriksson Examensarbete i matematik, 30 hp Handledare: Arianna Bottinelli Ämnesgranskare: David Sumpter Examinator: Denis Gaidashev Februari 2018 Department of Mathematics Uppsala University Examensarbete 30 hp Februari 2018 Modeling Information Transfer in High-Density Crowds Olle Eriksson Abstract Modeling Information Transfer in High-Density Crowds Olle Eriksson Teknisk- naturvetenskaplig fakultet UTH-enheten During large gatherings of people, such as concerts and sporting events, the crowd density can become very high in some areas. This causes a build-up of pressure as Besöksadress: people push against each other, which in turn can lead to potentially life-threatening Ångströmlaboratoriet Lägerhyddsvägen 1 collective phenomena such as crowd crushes and crowd turbulence. As testified by Hus 4, Plan 0 many reports and interviews, such phenomena are often exacerbated by the behavior of people in less dense parts of the crowd, who typically remain unaware of the Postadress: danger and therefore keep pushing towards their target. As the failure to effectively Box 536 751 21 Uppsala disseminate urgent information within a crowd makes it difficult to decrease the crowd density to safe levels, it is clearly important to study the nature of information Telefon: transfer in high-density crowds. 018 – 471 30 03 Telefax: The aim of this thesis is to investigate how information transfer between people in a 018 – 471 30 00 crowd is affected by crowd density and structure. High-density crowd conditions are simulated by combining models of information transfer with spatial self-propelled Hemsida: particle (SPP) models. First, I investigate some of the possible reasons why http://www.teknat.uu.se/student information transfer might fail within a dense crowd. Second, I examine to what extent effective information transfer can play a role in reducing crowd density by making individuals respond by acting in a certain way, e.g. stop pushing, when informed about an ongoing crowd crush. Results show how structural, environmental and psychological factors influence information transfer, indicating that it might be possible to prevent crowd crushes by raising awareness through, e.g., public information campaigns. Handledare: Arianna Bottinelli Ämnesgranskare: David Sumpter Examinator: ----- ----- Contents Abstract 2 Contents 3 1 Introduction 5 2 Background 8 2.1 Crowd disasters . .8 2.2 Pedestrian and crowd dynamics . .9 2.3 Models of information transfer . 11 3 Models and simulations 13 3.1 Modeling high density crowds . 13 3.1.1 Some useful definitions . 15 3.1.2 An asocial force model for high-density crowds . 17 3.1.3 List of notation . 19 3.2 Two models for information transfer . 20 3.2.1 The threshold model . 21 3.2.2 The dose model . 21 3.2.3 List of notation . 24 3.3 The spatial information model (SIM) . 25 3.3.1 Modeling behavioral response . 26 3.3.2 List of notation . 26 3.4 Numerical simulations . 27 3.4.1 Initial conditions and parameter values . 27 3.4.2 Verlet integration . 29 3 4 Results 31 4.1 Observed quantities . 32 4.2 Information transfer in the threshold model . 32 4.2.1 Effect of directionality . 36 4.3 Information transfer in the dose model . 37 4.3.1 Finite memory . 39 4.4 Behavioral response . 40 5 Discussion and conclusions 43 Bibliography 47 4 Chapter 1 Introduction As people become tightly packed together in a crowd, density can reach extreme levels where all individual control is lost. As the pressure from surrounding bodies increases, so does the risk of injury or of death from asphyxiation caused by chest compression. Crowd disasters have a long history, and the death toll of single incidents can be staggering. Contrary to common belief, deaths in crowd accidents are not usually due to stampedes, but are rather caused by compression asphyxiation in what is fittingly known as a crowd crush [1]. The history of crowd disasters reminds us that unless appro- priate measures are taken to ensure safety, a crowd can become a potential death trap. From reports and interviews we learn that a striking component of many crowd disasters is that the people at the back of the crowd keep pushing forward, oblivious to the fact that others are struggling for their lives a little farther ahead [1, 2, 3]. This observation seems to indicate that exception- ally high densities cause the break-down of efficient information transfer and coordination between pedestrians. This includes both visual-based information, as in dense crowds the field of view is clearly limited to a few neighbors, and also verbal information. Whereas effective practices such as crowd man- agement exist that aim to reduce the risks associated with mass gatherings through e.g. the use of crowd control barriers, the mechanisms that prevent verbal information from propagating within crowds are not well understood. By using mathematical models, it is possible to simulate dense crowds of various shapes and sizes [4]. However, existing models for crowd dynamics do not incorporate verbal information transfer. Likewise, there are many models for information transfer, but it is not well understood how the dynamics of 5 Figure 1.1: Dense crowd at the 2010 Love Parade electronic dance music festial in Duisburg, Germany, about 90 minutes before the infamous crowd disaster. Photo by Arne Müseler,licensed under CC BY-SA 3.0. these models is affected when they are embedded into physical space [5, 6]. As the connection between high-density and the failure of information transfer is not yet understood, the aim of this thesis is to use mathematical modeling to study the process of verbal information transfer in high-density crowds, as well as to investigate the possible reasons why it fails. In order to fulfill this aim, we first embed models of information transfer into a spatial crowd model in such a way that the latter provides the physical constraints that govern the dynamics of the former. We call the resulting hybrid model the spatial information model (SIM). For the spatial model, we use a modified social force model. Two different models of information transfer are considered. As a second step towards our goal, we use the SIM to investigate the reasons why information transfer fails in high-density crowds. This is done by simulating crowds composed of people with different attitudes and per- ceptiveness (represented in the SIM by using different parameter values). 6 The results indicate that small differences in crowd composition dictate if information reaches the entire crowd, or stays confined. Finally, we examine to what extent effective information transfer can play a role in reducing crowd density, and hence pressure levels, by making individuals respond in a certain way when informed about an ongoing crowd crush. We use the term behavioral response when referring to a response to information. Two behavioral responses are considered: (1) stop pushing and (2) turn around and walk away. The results indicate that both behavioral responses are indeed effective at reducing crowd pressure as long as a critical mass of informed individuals is reached. Chapter 2 presents some background on the subject of crowd disasters, before giving a brief introduction to pedestrian and crowd dynamics as well as models for information propagation. In Chapter 3 we introduce the SPP crowd model, as well as our two models for information transfer, before getting into the details of how to combine them into the SIM. In Chapter 4 we present the results of our investigations into why information fails in high- density crowds and the effectiveness of behavioral responses to information. Finally, Chapter 5 contains a discussion of the results as well as outlooks and concluding remarks. 7 Chapter 2 Background In this chapter, we briefly introduce the fundamental background for chap- ters 3 and 4, while at the same time providing some historical context and highlighting gaps in the literature that are relevant to this thesis. 2.1 Crowd disasters Crowds and mass gatherings are ubiquitous in human society. They form whenever lots of people come together, such as during concerts, sporting events, demonstrations and in busy city districts at rush hour, and usually present no imminent danger. People in crowds depend mainly on visual cues in a coordinated effort to navigate efficiently and to avoid bumping into each other [7]. However, coordinated movement gets increasingly difficult as crowds get denser, and becomes impossible at densities of about 7 people per square meter [1]. We use the term high-density crowd to refer to such dense crowd conditions. In high-density crowds, physical interacions and the desire to reach a (possibly common) destination are the dominant factors that govern crowd behavior. This causes the emergence of phenomena such as stop-and-go waves and crowd turbulence that may cause sudden forceful displacements of people over considerable distances [3]. The associated compression causes pressure that can reach dangerous levels and lead to compression asphyxiation within seconds, and the thermal insulation and heat provided by many bodies tightly packed together add to the danger [1, 8, 9]. Crowd accidents in which injury and/or death is due to high compression are known as crowd 8 crushes [10]. Crowd disasters have occured throughout human history. Examples in more recent times include the Hillsborough disaster in 1989, where 96 people died in a crowd crush during a soccer match between Liverpool and Not- tingham Forest at Hillsborough football stadium in Sheffield, England [11], and the Love Parade disaster in 2010, where 21 people died in a crowd crush during a electronic dance music festial in Duisburg, Germany [10] (see Fig- ure 1.1). A remarkable observation that has been made is that oftentimes people in a crowd are completely unaware of a crowd crush happening not too far away [1].

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