Managing Crowds in Hazards With Dynamic Grouping

—Emergency navigation algorithms for evacuees in confined spaces typically treat all evacuees in a homogeneous manner, using a common metric to select the best exit paths. In this paper, we present a quality of service (QoS) driven routing algorithm to cater to the needs of different types of evacuees based on age, mobility, and level of resistance to fatigue and hazard. Spatial information regarding the location and the spread of hazards is also integrated into the routing metrics to avoid situations where evacuees may be directed towards hazardous zones. Furthermore, rather than persisting with a single decision algorithm during an entire evacuation process, we suggest that evacuees may adapt their course of action with regard to their ongoing physical condition and environment. A widely tested routing protocol known as the Cognitive Packet Network (CPN) with random neural networks (RNN) and reinforcement learning is employed to collect information and provide advice to evacuees, ...

• Type: Journal
• Year: 2015
• Where: ACCESS
• Authors: Olumide J. Akinwande, Huibo Bi, Erol Gelenbe
• Keywords: ACCESS 2015, Artificial Intelligence