Emergency navigation, energy optimisation and cooperative algorithms for motion and evacuation

Abstract

The increasing concentration of human populations in modern urbanised societies has aggravated the frequency and destruction of both natural and manmade disasters, and has motivated considerable research over the last few decades. Accompanying the development of computing technology, emergency navigation algorithms in built environment have evolved from off-line algorithms that direct evacuees in accordance with pre-deployed static evacuation plans to on-line algorithms that dynamically calculate egress paths for evacuees. However, these algorithms normally consider evacuees in a homogeneous manner, and ignore the different requirements and relative risk of death among different groups of people caused by different mobilities, physical strength, health conditions and level of resistance to hazard. Therefore, this work aims to develop systems and algorithms to dynamically customise distinct paths for different categories of evacuees. To this end, we borrow the concept of Cognitive Packet Network (CPN) and adapt it to the context of emergency navigation. On top of the CPN framework, we design several routing metrics to calculate distinct egress paths for different categories of evacuees. To improve the inter and intra-group coordination, several cooperative strategies are proposed to further optimise the routes calculated by the proposed routing algorithm.

To provide a more accurate prediction to the congestion level of each egress path during an evacuation process under the effect of panic behaviours, we combine the CPN based routing algorithm with a G-network model to analyse the congestion level on a path via capturing the dynamics of diverse categories of evacuees under the influence of panic and re-routing decisions from the navigation system. Finally, we extend our work to large scale evacuations, and propose a G-network based emergency navigation algorithm to direct vehicles to safe areas in the aftermath of a large-scale disaster in an energy and time efficient manner.