Systematic Crowd Mobility Management to Avoid Catastrophic Disasters of Human Stampedes: The Case of Mina Hajj

Ibadah, Nisrine; Benavente-Peces, César; Minaoui, Khalid; Rziza, Mohammed; Oumsis, Mohammed

doi:10.5220/0010147700660073

Cited by 3 publications

(2 citation statements)

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“…Sabilirrasyad et al used Myo Armband for precise simulation of the stoning ritual by precisely determining the speed of throw for pelting stones at the pillars [44]. Ibadah et al also carried out a similar study considering Jamarat basin, stoning performance, and improved pilgrim management [45]. Ilyas analyzed the shape of Jamarat pillars and concluded that current elliptical pillars outperform other shapes of pillars [46].…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

2022

KSII TIIS

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The COVID-19 pandemic has resulted in a profound impact on large-scale gatherings throughout the world. Social distancing has become one of the most common measures to restrict the spread of the novel Coronavirus. Islamic pilgrimage attracts millions of pilgrims to Saudi Arabia annually. One of the mandatory rituals of pilgrimage is the symbolic stoning of the devil. Every pilgrim is required to perform this ritual within a specified time on three days of pilgrimage. This ritual is prone to congestion due to strict spatiotemporal requirements. We propose a pedestrian simulation model for implementing social distancing in the stoning ritual. An agent-based simulation is designed to analyze the impact of inter-queue and intra-queue spacing between adjacent pilgrims on the throughput and congestion during the stoning ritual. After analyzing several combinations of intra-queue and inter-queue spacings, we conclude that 25 queues with 1.5 meters of intra-queue spacing result in an optimal combination of throughput and congestion. The Ministry of Hajj in Saudi Arabia may benefit from these findings to manage and plan pilgrimage more effectively.

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Section: Related Workmentioning

confidence: 99%

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

2022

KSII TIIS

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“…This diversified nature of crowds would not have been a reality without the use of modern technologies such as wireless networking, computer vision, spatial computing, data analytics, mobile applications, immersive technologies, and crowd modelling and simulation [2]. The safety of the participants is of primary importance, and it is managed by observing crowd behavior and accurately identifying human activity [3]. Although there is a considerable investment made by the Saudi government towards wireless visual sensor networks with over 5000 cameras installed, along with scalar sensor technologies such as mobile phones Sensors 2021, 21, 8291 2 of 16 equipped with GPS sensors that are used for pilgrim tracking and monitoring, the monitoring of such an event is very challenging due to large numbers of pilgrims attending each year.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network

Habib

Hussain

Albattah

et al. 2021

Sensors

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Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently. Problem: As a result, these cameras generate an enormous amount of visual data through manual or offline monitoring, requiring numerous human resources for efficient tracking. Therefore, there is an urgent need to develop an intelligent and automatic system in order to efficiently monitor crowds and identify abnormal activity. Method: The existing method is incapable of extracting discriminative features from surveillance videos as pre-trained weights of different architectures were used. This paper develops a lightweight approach for accurately identifying violent activity in surveillance environments. As the first step of the proposed framework, a lightweight CNN model is trained on our own pilgrim’s dataset to detect pilgrims from the surveillance cameras. These preprocessed salient frames are passed to a lightweight CNN model for spatial features extraction in the second step. In the third step, a Long Short Term Memory network (LSTM) is developed to extract temporal features. Finally, in the last step, in the case of violent activity or accidents, the proposed system will generate an alarm in real time to inform law enforcement agencies to take appropriate action, thus helping to avoid accidents and stampedes. Results: We have conducted multiple experiments on two publicly available violent activity datasets, such as Surveillance Fight and Hockey Fight datasets; our proposed model achieved accuracies of 81.05 and 98.00, respectively.

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Surveying Challenges for Enhancing Precision in Hajj Crowd Simulation

N Alhawsawi,

Ahmad,

UR Rehman

et al. 2023

Preprint

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Systematic Crowd Mobility Management to Avoid Catastrophic Disasters of Human Stampedes: The Case of Mina Hajj

Cited by 3 publications

References 0 publications

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network

Surveying Challenges for Enhancing Precision in Hajj Crowd Simulation

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