  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.
