Indoor evacuation planning using a limited number of sensors

Abstract

This paper focuses on indoor evacuation path planning problem where the objective is to find evacuation paths for each evacuee such that the evacuation egress time is minimized. Since paths are dependent on the distribution of evacuees, initial positions of evacuees are required to find optimal paths during emergency. Instrumenting a building to obtain initial positions and count of people in a building is very challenging (and costly), and hence evacuation plans are prepared for a few expected distributions. Generally, a standard plan based on a predominant distribution of evacuees is posted on the walls inside an indoor facility for people to follow. However, the actual distribution may be another distribution from among the possible distributions. In this work, we consider the problem of finding the distribution that prevails at the evacuation time so that evacuees can be guided to follow the optimal paths (leading to minimum time) rather than following the standard plan. We propose a cost-effective solution to this problem by observing movement of people within a specified time period, labeled distribution detection window, using minimum number of optimally located sensors. This is in contrast to existing approaches which assume that unlimited sensors are available to instantaneously obtain the exact distribution of evacuees at the time of incidence. To our knowledge this paper presents the first formal evacuation planning approach that enables the user to optimally tradeoff the delay in distribution detection with the cost of the deployed sensor network used to obtain this distribution. Our approach is based on the popular heuristic denoted as Capacity Constrained Routing Planner (CCRP). Our approach is illustrated by a set of experiments on two case studies. The results demonstrate that evacuation plans obtained using minimum number of sensors are better than the standard plans and are comparable to evacuation plans computed using unlimited number of sensors.