This paper clarifies the factors that resulted in commuters being unable to return home and commuters' returning-home decision-making process at the time of the Great East Japan Earthquake using Twitter data. First, to extract the behavioural data from the tweet data, we identify each user's returning-home behaviour using support vector machines. Second, we create nonverbal explanatory factors using geo-tag data and verbal explanatory factors using tweet data. Following this, we model users' returning-home decision-making using a discrete choice model and clarify the factors quantitatively. Finally, we show the usefulness and the challenges of social media data for travel behaviour analysis.
- information extraction from social media data
- returning-home behaviour in a disaster
- travel behaviour analysis in a disaster
ASJC Scopus subject areas