Abstract:

Search and Rescue (SAR) operations are critical to the safety and well-being of individuals who visit state and national parks. In a missing hiker case, time is crucial as survival rates decrease each hour. It is estimated that approximately 4080 individuals become lost every year in the US. [21] The average cost of a SAR operation is $1,375 per person, and the cost of each mission is increasing yearly. Most SAR operations are based on rings of probabilistic distances. There is a need to incorporate a mechanistic, mathematical model that takes human behavior into consideration for determining SAR operations. Data from resources such as the International Search and Rescue Incident Database are analyzed to identify patterns in human behaviors and key geographic influences to develop a mechanistic model of missing persons. We use a discrete-time Markov Decision Process, where the lost individual’s state is used to determine a personal strategy for being found. The individual then interacts with the environment, where a utility function for that strategy over the geographic environment determines direction of travel. We take incident reports in various national parks as a case study to test our model. Implications are discussed for SAR, hiker survival training, and other aspects. The proposed model may be extended to the prediction of path-tracing of specific groups of people including experienced hikers or individuals who suffer from mental illnesses.

Year: 2019

Authors

Jose Alanis - San Joaquin Delta College

Madeline Brown - Scripps College

James Kitchens - Warren Wilson College

Jerry Magana - University of North Georgia

Camila Velastegui - Yachay Tech University 

Mugdha Thakur - Arizona State University 

Leon Arriola - University of Wisconsin

Baltazar Espinoza - Arizona State University 

Marisabel Rodriguez - Arizona State University 

R. Koester - Eastern Washington University 

Carlos Castillo-Garsow - Eastern Washington University