Title

Emergency response: Effect of human detection resolution on risks during indoor mass shooting events

Document Type

Article

Publication Date

1-22-2019

Publication Title

Safety Science

Volume

114

First page number:

160

Last page number:

170

Abstract

During an emergency, people rely on a few behavioral rules to save themselves. However, these rules may not be reliable, especially in indoor environments, as they are not fully aware of site information and their ability to think properly becomes fragile. Despite the increasing occurrence of mass shootings, there is no advanced systematic mechanism, which leverages recent technological advancements, in place to save people from this terror. To overcome these challenges, this research explores an integrated approach by combining resources (human monitoring; building information; and agent-based modeling). This research is to ameliorate public safety by investigating mass shooting scenarios through integration. Specifically, this study unveils the effect of sensory data on fatalities, along with safety mechanisms offered by systematic action plans. We designed six scenarios by varying spatial sensing coverage (comprehensive visual representation of the situation) from 0% to 100%. Each scenario was tested from 2000 to 2500 times to obtain statistical significance. Results indicate that even low sensing coverages (20% and 40%) show a slight improvement for safe evacuation and clear potential to reduce casualties within the first five minutes. Significant improvements are found in all cases (safe evacuation, casualties, rescuing people, and total survival) when the sensing coverages are high, around 60% to 100%. This study confirms the applicability of an integrated approach to identify a safety mechanism operating in place during a mass shooting incident. The findings could serve as a basis for future studies that can progressively improve emergency safety mechanisms based on available site resources.

Keywords

Emergency; Terror; Saftey; Awareness; Sensing; Intelligent system

Disciplines

Environmental Engineering

Language

English

UNLV article access

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