The Mechanism of Crowd Stampede Based on Case Statistics through SNA Method

Stampede is a concern of urban pubic security management. The current academic research focus is the identification of risk factors of trampling accidents and determination of correlation patterns and accident-causing mechanisms among stampede elements in order to effectively obtain the influencing factors of stampede and clarify the transmission routes of stampede risk factors. Previous index cases were scrutinized and analyzed in 78 typical stampedes from 2010 - 2019 based on "pedestrian-equipment-environment-management" framework, and 17 influencing factors of stampede by adopting a conceptual coding method were obscured. Then, the degree centrality, intermediate centrality and respective weights of the influencing factors were calculated based on the social network analysis (SNA) method. The influencing level of the factors was signified, and the transmission mechanism of risk in the system network was determined. The results reveal that the degree centrality and weight with conspicuous features of over-density of crowds, pedestrian swarming and falling, and insufficient on-site transactions contribute the most. This finding indicates that these factors play a relatively major role in the stampede system. Furthermore, the intermediate centrality of insufficient on-site transactions is the top factor, meaning that this factor has a strong controlling force in the incident system and considerably influences other factors. This study shows that the SNA method is feasible in analyzing the mechanism of stampede incidents, simultaneously addressing the shortcomings of the linear statistical model of factors and providing theoretical support for comprehensive control of crowd risk

A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

In emergency management for mass gatherings, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, the literature shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This paper presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data to validate our framework is described

Context-aware architecture using mobile phone simulator: a case study of an institution

Mobile phones nowadays are ubiquitous with our daily activities and the sensing ability of the device inspired us to propose context aware services to users in the academic environment. This paper presents the concept of a context aware solution using a proposed architecture from a theoretical perspective. The context-aware solution is a notification that delivers a text or call about certain information dissemination on events or activities that occurs as a result of unforeseen circumstances or situational changes in the institution on their mobile phones. The process is activated by a pop-up from the proxy server through the mobile portal database. This is followed up by a reminder from the server when necessary. This paper offers solution to a perennial problem that normally occur from traditional method of bill board or notice board placement of event such as examination time table or course schedule, as means of information dissemination to lecturers and students in the institution. The approach was simulated with phone simulator, and results shown to lecturers and students as presented in the paper were satisfactory

Enhanced context-aware framework for individual and crowd condition prediction

Context-aware framework is basic context-aware that utilizes contexts such as user with their individual activities, location and time, which are hidden information derived from smartphone sensors. These data are used to monitor a situation in a crowd scenario. Its application using embedded sensors has the potential to monitor tasks that are practically complicated to access. Inaccuracies observed in the individual activity recognition (IAR) due to faulty accelerometer data and data classification problem have led to its inefficiency when used for prediction. This study developed a solution to this problem by introducing a method of feature extraction and selection, which provides a higher accuracy by selecting only the relevant features and minimizing false negative rate (FNR) of IAR used for crowd condition prediction. The approach used was the enhanced context-aware framework (EHCAF) for the prediction of human movement activities during an emergency. Three new methods to ensure high accuracy and low FNR were introduced. Firstly, an improved statistical-based time-frequency domain (SBTFD) representing and extracting hidden context information from sensor signals with improved accuracy was introduced. Secondly, a feature selection method (FSM) to achieve improved accuracy with statistical-based time-frequency domain (SBTFD) and low false negative rate was used. Finally, a method for individual behaviour estimation (IBE) and crowd condition prediction in which the threshold and crowd density determination (CDD) was developed and used, achieved a low false negative rate. The approach showed that the individual behaviour estimation used the best selected features, flow velocity estimation and direction to determine the disparity value of individual abnormality behaviour in a crowd. These were used for individual and crowd density determination evaluation in terms of inflow, outflow and crowd turbulence during an emergency. Classifiers were used to confirm features ability to differentiate individual activity recognition data class. Experimenting SBTFD with decision tree (J48) classifier produced a maximum of 99:2% accuracy and 3:3% false negative rate. The individual classes were classified based on 7 best features, which produced a reduction in dimension, increased accuracy to 99:1% and had a low false negative rate (FNR) of 2:8%. In conclusion, the enhanced context-aware framework that was developed in this research proved to be a viable solution for individual and crowd condition prediction in our society

Artificial intelligence and cloud-based collaborative platforms for managing disaster, extreme weather and emergency operations

YesNatural disasters are often unpredictable and therefore there is a need for quick and effective response to save lives and infrastructure. Hence, this study is aimed at achieving timely, anticipated and effective response throughout the cycle of a disaster, extreme weather and emergency operations management with the help of advanced technologies. This study proposes a novel, evidence-based framework (4-AIDE) that highlights the role of artificial intelligence (AI) and cloud-based collaborative platforms in disaster, extreme weather and emergency situations. A qualitative approach underpinned by organizational information processing theory (OIPT) is employed to design, develop and conduct semi-structured interviews with 33 respondents having experience in AI and cloud computing industries during emergency and extreme weather situations. For analysing the collected data, axial, open and selective coding is used that further develop themes, propositions and an evidence-based framework. The study findings indicate that AI and cloud-based collaborative platforms offer a structured and logical approach to enable two-way, algorithm-based communication to collect, analyse and design effective management strategies for disaster and extreme weather situations. Managers of public systems or businesses can collect and analyse data to predict possible outcomes and take necessary actions in an extreme weather situation. Communities and societies can be more resilient by transmitting and receiving data to AI and cloud-based collaborative platforms. These actions can also help policymakers identify critical pockets and guide administration for their necessary preparation for unexpected, extreme weather, and emergency events.This study has been supported by the Area of Excellence AI, Data Science & Business at NEOMA Business School, France under the fund number 416005

A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target

Crowds can be subject to intrinsic and extrinsic sources of risk, and previous records have shown that, in the absence of adequate safety measures, these sources of risk can jeopardise human lives. To mitigate these risks, we propose that implementation of multiple layers of safety measures for crowds—what we label The Swiss Cheese Model of Crowd Safety—should become the norm for crowd safety practice. Such system incorporates a multitude of safety protection layers including regulations and policymaking, planning and risk assessment, operational control, community preparedness, and incident response. The underlying premise of such model is that when one (or multiple) layer(s) of safety protection fail(s), the other layer(s) can still prevent an accident. In practice, such model requires a more effective implementation of technology, which can enable provision of real-time data, improved communication and coordination, and efficient incident response. Moreover, implementation of this model necessitates more attention to the overlooked role of public education, awareness raising, and promoting crowd safety culture at broad community levels, as one of last lines of defence against catastrophic outcomes for crowds. Widespread safety culture and awareness has the potential to empower individuals with the knowledge and skills that can prevent such outcomes or mitigate their impacts, when all other (exogenous) layers of protection (such as planning and operational control) fail. This requires safety campaigns and development of widespread educational programs. We conclude that, there is no panacea solution to the crowd safety problem, but a holistic multi-layered safety system that utilises active participation of all potential stakeholders can significantly reduce the likelihood of disastrous accidents. At a global level, we need to target a Vision Zero of Crowd Safety, i.e., set a global initiative of bringing deaths and severe injuries in crowded spaces to zero by a set year

Physical crowds and psychological crowds: applying self-categorization theory to computer simulation of collective behaviour

Computer models are used to simulate pedestrian behaviour for safety at mass events. Previous research has indicated differences between physical crowds of co-present individuals, and psychological crowds who mobilise collective behaviour through a shared social identity. This thesis aimed to examine the assumptions models use about crowds, conduct two studies of crowd movement to ascertain the behavioural signatures of psychological crowds, and implement these into a theoretically-driven model of crowd behaviour. A systematic review of crowd modelling literature is presented which explores the assumptions about crowd behaviour being used in current models. This review demonstrates that models portray the crowd as either an identical mass with no inter-personal connections, unique individuals with no connections to others, or as small groups within a crowd. Thus, no models have incorporated the role of self-categorisation theory needed to simulate collective behaviour. The empirical research in this thesis aimed to determine the behavioural effects of self-categorisation on pedestrian movement. Findings from a first study illustrate that, in comparison to a physical crowd, perception of shared social identities in the psychological crowd motivated participants to maintain close proximity with ingroup members through regulation of their speed and distance walked. A second study showed that collective self-organisation seemed to be increased by the presence of an outgroup, causing ingroup members to tighten formation to avoid splitting up. Finally, a computer model is presented which implements the quantified behavioural effects of self-categorisation found in the behavioural studies. A self-categorisation parameter is introduced to simulate ingroup members self-organising to remain together. This is compared to a physical crowd simulation with group identities absent. The results demonstrate that the self-categorisation parameter provides more accurate simulation of psychological crowd behaviour. Thus, it is argued that models should implement self-categorisation into simulations of psychological crowds to increase safety at mass events