Pedestrian Flow Simulation Validation and Verification Techniques

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analyzing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge. The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.Comment: 19 pages, 10 figure

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

The crowd psychology of the Hajj

This thesis is the first study of the crowd psychology of the annual Hajj, or pilgrimage to Mecca (Makkah) in Saudi Arabia, to employ self-categorization theory (SCT). The thesis aims to document and understand the perspective of pilgrims from a social psychological point of view, since no one has done that before, as well as to understand the perceptions of the Hajj management. Specifically, the thesis focuses on crowd perceptions, feelings of safety and the reasons for these feelings, and relations between subgroups in the crowd and between pilgrims and management. A literature review in Chapter two highlights the history and culture of the Hajj and the issues in managing the Hajj. Academic perspectives on crowd psychology are discussed in chapter three. Chapters four and five present respectively a UK pilot study of pilgrims and a field pilot study of pilgrims and management. Chapter six (the main interview study with pilgrims) indicates that despite the inconveniences, participants felt safe, secure and wellbeing inside the Grand Mosque during Hajj. Chapter seven (the main interview study with Hajj management) explores the participants’ understanding of crowd behaviour, crowd psychology and its relation to safety, danger and their own role. In Chapter eight (the major study of the thesis), a survey of 1194 pilgrims at the Hajj found that identification with the crowd predicted enjoyment of the crowd. Also, for those high in identification with the crowd, crowd density increased perceptions of safety. Perceived support was found to mediate these positive effects of social identity on feeling safe. Chapter nine critically explores the findings of the thesis and discusses them in relation to relevant literature. It also reflects on the implications of the study for the theory of crowd psychology, and considers what lessons there might be for the management of the Hajj. This chapter concludes the thesis and outlines suggestions for further research

Management information system for hajj pilgrim's total wellness

Hajj is a spiritual journey which require physical and mental preparation since pilgrims need to face hectic activity, extreme temperature and exhaustive environment during Hajj. Recently, there are few instruments and models that relate to wellness however they are too general and not specific for certain event or religious rituals. Besides, existing management system only focuses on treatment and emphasize on physical, physiological and medical history only. Thus, the purpose of this study was to develop instrument, model, prescription and management information system specific for Hajj Pilgrim’s Wellness. Sequential exploratory design were used trough out this research. Eight construct were established from the interview conducted with 5 panel of expert consist of physical activity, physical care, healthy eating, intrapersonal, interpersonal, knowledge, mental toughness and relationship with Creator and creatures. Items for each construct were determine based on past study and need analysis. A survey was conducted to 300 respondents from six mosques in Johor Bahru district. The data gathered were analyzed using Rasch Measurement analysis. The findings showed instrument fit the model in terms of construct validity, item and person reliability, rating scale, dimensional and item fit. Besides, there were significant differences between wellness based on demographic characteristics including age, health status and occupation except gender. Next, a model was developed using average of item logit to determine the contribution factors hierarchy towards wellness level. Then, prescription was developed based on previous research and content validity were gathered from three panel of experts. Finally, a web based system was developed and the usability of the developed system was measured using IsoMetricS questionnaire. Thus, it was recommended that the Ministry of Health and Tabung Haji used and promote awareness among hajj pilgrims by referring to the model in the success of Hajj practices

Virtual reality training for Hajj pilgrims as an innovative community translation dissemination medium

During the Islamic pilgrimage known as Hajj, Muslim pilgrims from all over the world, with many different backgrounds, gather together and coexist in the city of Mecca in Saudi Arabia. Managing a large and diverse congregation for the safe and successful completion of Hajj requires effective communication channels between speakers of the mainstream languages and international pilgrims or non-Arabic-speaking pilgrims. The focus of the study is on the use of innovative media in community translation (CT) dissemination methods and will determine which CT dissemination media are the most effective for English-speaking Hajj pilgrims. The study compares three forms of media: the booklet and video guides from the official Mnask Academy media produced by Hajj authorities; and the prototype of this study, an immersive virtual reality-based Hajj training media “VR-Hajj”. The methodology of the study consisted of three stages, starting with the development of assessment tools. Community translation usability (CTX) and medium usability (MX) for the different community translation dissemination media, which were based on the literature on CT studies and user-centred translation (UCT) studies, as well as usability studies (UX). The next stage was prototyping, which involved the collaboration between the researcher and virtual reality experts (developers and designers). The final stage was testing the three community translation dissemination media mentioned earlier with English-speaking Muslim users. A total of 96 Muslim respondents were surveyed, three groups were formed, and each participant evaluated a community translation dissemination medium. The self-administered questionnaire elicited perceptions and feedback about CTX and MX from the three groups. Quantitative data was processed using Statistical Package for the Social Sciences (SPSS), while qualitative data was analysed using the Thematic Analysis (TA) method. The results of the present study revealed significant differences between the levels of community translation perception and medium usability achieved by participants from each group. In addition, the results revealed the shortcomings of the conventional Mnask Academy training media currently in use, as well as the promising advantages of using innovative immersive virtual reality technology for Hajj training. The study concludes that immersive virtual reality technology, which allows pilgrims to mentally travel to the Hajj area, is more effective for understanding community translation, Hajj rituals and related cultural aspects than passively-created community translation media

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