Advances in technology have given rise to the widespread use of small unmanned aerial systems (sUAS), more commonly known as ‘drones.’ The sUAS market is expected to continue to increase at a rapid pace, with the FAA forecasting around 8,000 registrations monthly (FAA, 2019). High profile drone incidents include use in an attack on the Venezuelan president, an undetected landing on the property of the White House, and use in dropping crude explosives on troops in the Middle East (Gramer, 2017; Grossman, 2018; Wallace & Loffi, 2015). The rate of proliferation and high-performance characteristics of these drones has raised serious concerns for safety in high-density outdoor events. Counter-unmanned aerial systems are currently illegal for all but a few Federal entities within the U.S., leaving private and public entities at risk. This exploratory research investigates several legal facility and patron behavioral interventions to reduce possible casualties during a drone attack by using AnyLogic simulation modeling in an amusement park scenario. Data from this experiment suggest that behavioral interventions implemented 30 seconds before a drone attack can reduce casualties by more than 55%, and up to 62% casualty reductions can be realized with a 60-second implementation time. Testing suggests that venue design considerations, such as a reduction in hard corners, covered high-density areas, and smoother area transitions can synergistically reduce casualties when used in conjunction with a warning system. While casualty mitigation did occur throughout the study, active threat interdiction methods would be necessary to design a system that may prevent casualties overall.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13341860 |
| Date | 15 December 2020 |
| Creators | Travis L Cline (9739406) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Mitigating_Drone_Attacks_For_Large_High-Density_Events/13341860 |
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