使用移动终端位置数据构建疏散演化模式和决策：以飓风“艾玛”为例
Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma
摘要：
了解飓风疏散期间个人的行为对于希望为自然灾害做好准备的地方，州和政府机构至关重要。人类决策程序的复杂性和缺乏此类灾害的数据是使飓风疏散研究具有挑战性的主要原因。在本文中，我们利用一个大的移动电话基于位置的服务（LBS）的数据，以构建在飓风厄玛登陆的疏散模式。通过对超过110亿次移动电话位置目击使用我们提出的框架，我们能够捕获居住在佛罗里达州的807，623名智能手机用户的疏散决定。我们研究了用户的疏散决策，出发和返回日期分布，以及目的地选择。除了这些决定，我们实证研究疏散秩序和低洼的居民区对个人的疏散决策的影响。我们的分析显示，57.92%的人生活在强制疏散区撤离住所，而这一比例为32.98%，33.68%的人生活在没有疏散命令和自愿疏散命令的地区。此外，我们的分析揭示了在模拟疏散决策选择的个人的移动行为的重要性。历史移动行为信息，如每个人的出行次数和个人的位置轨迹所覆盖的空间区域估计显着在我们的选择模型，并提高模型的整体精度显着。
英文摘要：
Understanding individuals' behavior during hurricane evacuation is of paramount importance for local, state, and government agencies hoping to be prepared for natural disasters. Complexities involved with human decision-making procedures and lack of data for such disasters are the main reasons that make hurricane evacuation studies challenging. In this paper, we utilized a large mobile phone Location-Based Services (LBS) data to construct the evacuation pattern during the landfall of Hurricane Irma. By employing our proposed framework on more than 11 billion mobile phone location sightings, we were able to capture the evacuation decision of 807,623 smartphone users who were living within the state of Florida. We studied users' evacuation decisions, departure and reentry date distribution, and destination choice. In addition to these decisions, we empirically examined the influence of evacuation order and low-lying residential areas on individuals' evacuation decisions. Our analysis revealed that 57.92% of people living in mandatory evacuation zones evacuated their residences while this ratio was 32.98% and 33.68% for people living in areas with no evacuation order and voluntary evacuation order, respectively. Moreover, our analysis revealed the importance of the individuals' mobility behavior in modeling the evacuation decision choice. Historical mobility behavior information such as number of trips taken by each individual and the spatial area covered by individuals' location trajectory estimated significant in our choice model and improve the overall accuracy of the model significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
