The effectiveness of naive optimization of the egress path for an active-shooter scenario

Abstract

There have been 130 mass shootings in the United States from 1982 to June, 2022 according to the Mother Jones database of active shooter events. In these critical scenarios, making the right decisions while evacuating can be the difference between life and death. However, emergency evacuation is intensely stressful, which along with lack of verifiable real-time information may lead to costly incorrect decisions. In this paper, we demonstrate the effectiveness of a non-homogeneous semi-Markov-Decision-Process (NHSMDP) based naive algorithm that relies on prior knowledge about the layout of a building and uses recurring updates of the shooter's location (based on automatic processing of images from a camera network) to provide an optimized egress plan for evacuees. While emergency evacuations due to fire and natural disasters are well researched, the novelty of this work is in the response to a threat that moves either purposefully or randomly through the building and in incorporating the ability for an evacuee to wait for danger to pass before beginning egress and during the process of evacuation. This ability to include sojourn times in the optimized scheme is due to the NHSMDP formulation and is a notable augmentation to the current state-of-the-art. We show that following this algorithm can reduce casualties by 56% and the time spent by evacuees in the shooter's line of sight by 52% compared to an intuitive natural response guided by expert advice.