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.
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.
@cite_60 @cite_70 @cite_42 @cite_63 @cite_31 @cite_10
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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. 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 Hajj is one of the largest mass gatherings where Muslims from all over the world gather in Makah each year for pilgrimage. A mass assembly of such scale bears a huge risk of disaster either natural or man-made. In the past few years, thousands of casualties have occurred while performing different Hajj rituals, especially during the Circumambulation of Kaba (Tawaf) due to stampede or chaos. During such calamitous situations, an appropriate evacuation strategy can help resolve the problem and mitigate further risk of causalities. It is however a daunting research problem to identify an optimal course of action based on several constraints. Modeling and analyzing such a problem of real-time and spatially explicit complexity requires a microscale crowd simulation and analysis framework. Which not only allows the modeler to express the spatial dimensions and features of the environment in real scale, but also provides modalities to capture complex crowd behaviors. In this paper, we propose an Agent-based Crowd Simulation & Analysis framework that incorporates the use of Anylogic Pedestrian library and integrates interoperate Anylogic Simulation environment with the external modules for optimization and analysis. Hence provides a runtime environment for analyzing complex situations, e.g., emergency evacuation strategies. The key features of the proposed framework include: (i) Ability to model large crowd in a spatially explicit environment at real-scale; (ii) Simulation of complex crowd behavior such as emergency evacuation; (iii) Interoperability of optimization and analysis modules with simulation runtime for evaluating evacuation strategies. We present a case study of Hajj scenario as a proof of concept and a test bed for identifying and evaluating optimal strategies for crowd evacuation Without sufficient preparation and on-site management, the mass-scale unexpected huge human crowd is a serious threat to public safety. A recent impressive tragedy is the 2014 Shanghai Stampede, where 36 people died and 49 were injured in celebration of the New Year’s Eve on December 31st 2014 in the Shanghai Bund. Due to the innately stochastic and complicated individual movement, it is not easy to predict collective gatherings, which can potentially leads to crowd events. In this chapter, with leveraging the big data generated on Baidu Maps, we propose a novel approach to early warning such potential crowd disasters, which has profound public benefits. An insightful observation is that, with the prevalence and convenience of web map service, users usually search on the Baidu Maps to plan a routine, which reveals the users’ future destinations. Therefore, aggregating users’ query data on Baidu Maps can obtain priori and indication information for estimating future human population in a specific area ahead of time. Our careful analysis and deep investigation on the Baidu Maps’ data on various events also demonstrate a strong correlation pattern between the number of map query and the number of positioning in an area. Based on such observation, we propose a decision method utilizing query data on Baidu Maps to invoke warnings for potential crowd events about 1 3 h in advance. Then we also construct a machine learning model with heterogeneous data (such as query data and positioning data) to quantitatively measure the risk of the potential crowd disasters. We evaluate the effectiveness of our methods on the data of Baidu Maps.