An agent-based model reveals lost person behavior based on data from wilderness search and rescue.
An agent-based model reveals lost person behavior based on data from wilderness search and rescue.
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DOI：
10.1038/s41598-022-09502-4
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发表时间：
2022-04-07
影响因子：
4.6
通讯作者：
Abaid N
中科院分区：
文献类型：
--
作者：
Hashimoto A;Heintzman L;Koester R;Abaid N
据报道，美国每年有数千人在荒野中失踪，尽快找到这些失踪人员取决于协调的搜索和救援行动。随着时间的推移，搜索区域扩大，生存率下降，搜索人员面临着在短时间内搜索大面积区域的日益艰巨的任务。为了优化搜索过程，可以结合当前的搜索和救援实践，使用失踪人员行为与地形有关的数学模型。在本文中，我们介绍了一个基于代理的模型，迷失的人的行为，允许代理移动已知的景观与行为定义为一个随机变量的独立实现。行为随机变量从六个已知的失踪人员重新定位策略的分布中选择，以模拟代理的轨迹。我们系统地模拟了一系列可能的行为分布，并找到了一个最适合的行为配置文件与国际搜索和救援事件数据库的徒步旅行者。我们验证这些结果与留一法分析。这项工作是第一个时间离散模型丢失的人动态验证数据从真实的SAR事件，并有可能改善目前的方法，荒野SAR。
Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent’s trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR.
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