Abstract In large crowd events, there is always a potential possibility that a stampede accident will occur. The accident may cause injuries or even death. Approaches for controlling crowd flows and predicting dangerous congestion spots would be a boon to on-site authorities to manage the crowd and to prevent fatal accidents. One of the most popular approaches is real-time crowd simulation based on position data from personal Global Positioning System (GPS) devices. However, the accuracy of spatial data varies for different GPS devices, and it is also affected by an environment in which an event takes place. In this paper, we would like to assess the effect of position errors on stampede prediction. We propose an Automatic Real-time dEtection of Stampedes (ARES) method to predict stampedes for large events. We implement three different stampede assessment methods in Menge framework and incorporate position errors. Our analysis suggests that the probability of simulated stampede changes significantly with the increase of the magnitude of position errors, which cannot be eliminated entirely with the help of classic techniques, such as the Kalman filter. Thus, it is our position that novel stampede assessment methods should be developed, focusing on the detection of position noise and the elimination of its effect.
@cite_60: Using published field data on accidents in crowds involving either the trampling or crushing of pedestrians, a theory has been developed to model similar dangerous situations. Whether a particular situation is fatal is statistical, but general rules may be established. In cases involving trampling, which occurs when pedestrians are moving, it was found that the density of pedestrians is an important isotropic quantity that appears to determine the probability of a fatal accident. Previous studies have also identified the density of a crowd as determining the probability of crushing within a stationary crowd, although different densities are involved in the initiation of these two types of accidents. In a moving crowd, trampling will occur before crushing, but trampling does not occur in a static crowd. The cross-sectional size of pedestrians within the crowd is important in determining whether a particular density is dangerous in both types of accidents. Consequently, comparisons of accidents involving different groups of pedestrians, possibly in different countries, need to be standardized for the characteristics of the group.
@cite_70: Published data on accidents in human crowds that occurred over the past decade show that one of the fatal consequences of crowding was asphyxia, that is by crushing. Here a study was made of our ability to predict human crowd pressures at barriers and thereby be able to take preventive action before crushing causes injury. Utilizing the standard forward-backward autoregressive modeling techniques for spectral analysis of a measured signal, predictions of pressures generated by very high densities of pedestrians have been formulated. The study suggests that several minutes are available for corrective action to be taken to avoid an accident involving crushing of pedestrians. Language: en
@cite_42: Starting with a short review of the available literature in the field of pedestrian and evacuation research, an overview is given over the observed collective phenomena in pedestrian crowds. This includes lane formation in corridors and oscillations at bottlenecks in normal situations, while different kinds of blocked states are produced in panic situations. By means of molecular-dynamic-like microsimulations based on a generalized force model of interactive pedestrian dynamics, the spatio-temporal patterns in pedestrian crowds are successfully reproduced and interpreted as self-organized phenomena. In contrast to previous socio-psychological approaches, this allows a physical understanding of the observations. Despite the significantly different phenomena occuring in normal and panic situations, the main effects can be described by a unified model containing only well interpretable and plausible terms. The transition between the “rational” normal behavior and the apparently “irrational” panic behavior is controlled by a single parameter, the “nervousness”, which influences fluctuation strengths, desired speeds, and the tendency of herding. Thereby, it causes paradoxial effects like “freezing by heating”, “faster is slower”, and the ignorance of available exits. Nevertheless, there are measures to improve pedestrian flows, both in normal and panic situations. For example, the suitable placement of columns can help, although they reduce the accessible space.
@cite_63: The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. , Phys. Rev. Lett. 97 (2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. , Phys. Rev. E 75 (2007) 046109]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then, "crowd turbulence" sets in, which may trigger crowd disasters. For this reason, it is important to operate pedestrian facilities sufficiently below their maximum capacity and to take measures to improve crowd safety, some of which are discussed in the end.
@cite_31: The purpose of this paper is to examine a number of sports stadia disasters involving British fans since 1971. While each of the four incidents described (Ibrox 1971, Bradford 1985, Heysel 1985, Hillsborough 1989) can be described as crises in their own right (and led to the deaths of about 250 people in total) it is argued here that they are the effect of a wider crisis of man agement within the football (soccer) industry, rooted in poor and inadequate manage ment of sports stadia and the crowds that use them. It should be noted from the outset, however, that this is not simply a British phenomena, as the tragedies in Mexico City (1985) and Bastia (1992) indicate and that findings from this research should be of use to all involved in the management of sporting events.
@cite_10: With more and more people attending public gatherings, and with a continuous rise in crowd density in urban areas, the management of crowd has become more challenging than ever before. Every year, many people lose their lives due to inefficient crowd planning and management. Crowd management is an interdisciplinary area, and it requires understanding of engineering and technological aspects, along with an understanding of crowd behavior and crowd flow management, i.e. psychological and sociological aspects. This paper presents a broad, but not exhaustive overview of the recent technological advancements in the area of crowd planning and monitoring techniques for an effective crowd management system. It discusses the crowd modeling aspects during the planning of crowded scenario, and the technological advancements in crowd data acquisition techniques [based on Vision, Wireless Radio-Frequency (RF) and Web Social-media data mining technologies] during execution of crowded event. The paper also considers technological applications in some highly crowded scenarios on earth as case studies, along with future research directions in the area.
To analyze the risk of stampedes, a number of stampede measuring techniques were presented in the literature. @cite_10 , @cite_63 derived crowd pressure formulas (by analyzing video recordings of the crowd disaster during the Hajj in 2006) and discovered crowd turbulence phenomenon, in which physical contacts among people are transferred and accumulated. People try to escape the crowd, which causes crowd panic. Such phenomenon may trigger crowd disasters, such as stampedes. Lee and Hughes @cite_70 developed a crowd pressure measuring technique using the standard forward-backward auto-regressive modeling approach. Elliott and Smith @cite_31 examined some sporting disasters and found the relationship between the accidents and the inter-person forces. @cite_42 introduced social and physical forces among pedestrians and then treated each pedestrian as a particle abiding Newton's laws. Lee and Hughes @cite_60 studied the relationship between crushing situation and people density, which gives the typical threshold of people density of different nationalities. However, none of these work has taken into account the impact of GPS noise on the prediction of stampedes.