City Research Online

Abstract

Crowds are an inevitable part of society. Sometimes crowding, rather a degree of congestion, can benefit lifestyles in certain ways, particularly from a business and economical point of view. But at other times, crowding is detrimental to social progress. While businesses may prosper in areas with high human traffic, many lives are also lost due to crowding. Some of the reasons crowds are organised are for social gatherings (musical corsets, beach fronts), business activities (restaurants, fairs), sports, religious activities, political rallies, funeral and wedding processions, and so on. Managing crowds effectively and efficiently remains a challenging issue.

Tens of thousands of people have lost their lives due to the poor organisation of crowds. Several stampedes have taken place in both developed and developing countries. The reason for these disasters can be attributed to poor organisation, lack of infrastructure and technology. The literature review finds some studies aimed at providing ways to manage crowds. However, these studies concentrate on limited aspects of crowd management, and do not provide a comprehensive solution for all issues related to crowding.

This research presents a comprehensive framework leveraging modern technologies for effective crowd management, addressing the multifaceted challenges of handling large gatherings through a staged approach, including privacy and security considerations. It aims to systematically address crowd management issues from planning to post-event stages, each tailored to meet specific requirements.

In the Planning Stage, the focus is on establishing vital infrastructure and crisis management strategies, highlighted by the creation of an innovative automatic classification algorithm using machine learning and deep learning. This algorithm assesses event reservations based on criteria like event type and expected attendance, marking a significant advancement in structured event planning.

The Organization and Monitoring Stage integrates cutting-edge technologies like IoT, AI, drones, and fog and cloud computing, enhancing event organization and ensuring detailed monitoring. Features such as real-time data collection via IoT and smartphones, rapid data processing through fog computing, and automated access control exemplify the stage's sophistication. Algorithms for analyzing drone images and incident reports further boost situational awareness.

The Flow Regulation Stage shifts attention to managing crowd flow with digital tools for precise gateway control, essential for avoiding bottlenecks and ensuring orderly, safe movement. Additionally, the Additional Issues Stage encompasses health monitoring and waste management, emphasizing a holistic strategy that considers environmental and health impacts post-event.

Significantly, the research explores privacy and security solutions for crowdgenerated data, introducing methods like Private Information Retrieval (PIR) and Double Protecting Approach for data privacy, utilizing fog computing to enhance security without compromising personal information integrity.

This framework represents a pivotal advancement in crowd management, combining planning, organization, flow regulation, and privacy/security measures into a cohesive, technology-driven strategy. It not only addresses current crowd management challenges but also lays the groundwork for future innovations, offering a holistic approach to managing crowds effectively and securely.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science T Technology T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Engineering School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

Export

Downloads