Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior
Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior
复制标题
使用个人灾前网络搜索行为的表示来预测疏散决策
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
S. Ukkusuri
中科院分区：
文献类型：
--
作者：
T. Yabe;K. Tsubouchi;Toru Shimizu;Y. Sekimoto;S. Ukkusuri
在灾害发生之前预测个人的疏散决定对于规划第一响应策略至关重要。除了灾后疏散行为分析的研究外，还有各种工作试图预先预测疏散决策。然而，这些预测方法中的大多数需要用于校准的真实的时间位置数据，由于隐私问题的增加，这些数据变得越来越难以获得。与此同时，匿名用户的网络搜索查询已被网络公司收集。尽管这些数据引起的隐私问题较少，但它们在各种应用中的利用率却很低。在这项研究中，我们调查的网络搜索数据观察到的灾难之前，是否可以用来预测疏散决策。更具体地说，我们利用基于会话的查询编码器，学习每个用户的网络搜索行为的表示撤离之前。我们所提出的方法进行了实证测试，使用网络搜索数据收集受日本大洪水影响的用户。使用从同一组用户的移动的电话收集的位置数据作为地面实况来验证结果。我们发现，疏散决策可以准确地预测（84%），仅使用用户的灾前网络搜索数据作为输入。本研究提出了一种不需要高度敏感的位置数据的疏散预测方法，可以帮助地方政府制定有效的第一响应策略。
Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a session-based query encoder that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies.