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Predicting the evacuation decisions of individuals before the disasterstrikes is crucial for planning first response strategies. In addition to thestudies on post-disaster analysis of evacuation behavior, there are variousworks that attempt to predict the evacuation decisions beforehand. Most ofthese predictive methods, however, require real time location data forcalibration, which are becoming much harder to obtain due to the rising privacyconcerns. Meanwhile, web search queries of anonymous users have been collectedby web companies. Although such data raise less privacy concerns, they havebeen under-utilized for various applications. In this study, we investigatewhether web search data observed prior to the disaster can be used to predictthe evacuation decisions. More specifically, we utilize a "session-based queryencoder" that learns the representations of each user's web search behaviorprior to evacuation. Our proposed approach is empirically tested using websearch data collected from users affected by a major flood in Japan. Resultsare validated using location data collected from mobile phones of the same setof users as ground truth. We show that evacuation decisions can be accuratelypredicted (84%) using only the users' pre-disaster web search data as input.This study proposes an alternative method for evacuation prediction that doesnot require highly sensitive location data, which can assist local governmentsto prepare effective first response strategies.

Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior