Situational Awareness and Decision-Making Support for Crowd Management in Mina, A Grand Challenge Project Funded by the Ministry of Education
Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.
Overview
Abstract
Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.
The objective of this project is to tackle the challenge of developing the “Situational Awareness and Predictive Decision Support” System using enhanced data collection, advanced predictive data analytics, and real-time predictive crowd dynamics modeling and simulation. The ability of crowd managers to make accurate, timely, and effective decisions will be evaluated using the developed Situational Awareness tool. To prove the capability of our concept design, we will design, build and evaluate the Situational Awareness tool for Mina phase during Hajj season. The team believes that the output of this project will improve the safety and efficiency of crowd management processes to enable Saudi Arabia to achieve its Vision 2030 of hosting more than 30 million Umrah and Hajj visitors every year.
Brief Biography
Emad Felemban is an associate professor in the Computer engineering department of Umm Al-Qura University. He is the founder of Crowd Management Technologies, a consultation office specialized in applying advanced technologies for crowd and mobility management. He graduated from Ohio State University with PhD and Master degrees in 2009 and 2003, respectively. His research interests include Wireless Sensor Networks, IoT, Optimization, and Scheduling, Mobility, and Crowd Management. He won many research funding projects from KACST, NPSTI, SABIC, and the Ministry of Education. He is a consultant with the Ministry of Hajj and Umrah..
