Enhancing Pilgrimage Safety and Efficiency in Makkah and Madinah Using AI and Edge Computing

Efficiently managing crowds during high pilgrim seasons in the Holy Mosques of Mecca and Medina is a significant challenge. Traditional crowd management methods, which are predominantly reactive and rely heavily on manual interventions, are often slow, prone to delays, and inadequate in preventing dangerous conditions such as overcrowding and traffic congestion—precursors to stampedes. This research explores the integration of Artificial Intelligence (AI) and edge computing to enhance crowd management by enabling real-time processing and reducing decision-making latencies. Advanced AI technologies facilitate security and automation through real-time monitoring, surveillance, crowd behavior analysis and prediction, and movement optimization. Edge computing complements this by processing data closer to its source, avoiding the delays and resource-intensive nature of centralized cloud systems. This approach significantly improves the efficiency of monitoring crowd density, forecasting bottlenecks, and implementing automated measures during major events like Hajj and Umrah. In this paper, we explore how the deployment of AI within edge computing can enhance response times, strengthen security, and improve network efficiency in managing crowds during Hajj and Umrah. Multiple case studies and scenarios are designed to illustrate how edge computing can effectively mitigate overcrowding in real-life Hajj and Umrah scenarios, showcasing its capability to process data locally, reduce latency, and enable rapid responses to critical situations.