  Social media platforms, such as Twitter, provide a totally new perspective in
dealing with the traffic problems and is anticipated to complement the
traditional methods. The geo-tagged tweets can provide the Twitter users'
location information and is being applied in traveler behavior analysis. This
paper explores the full potentials of Twitter in deriving travel behavior
information and conducts a case study in Manhattan Area. A systematic method is
proposed to extract displacement information from Twitter locations. Our study
shows that Twitter has a unique demographics which combine not only local
residents but also the tourists or passengers. For individual user, Twitter can
uncover his/her travel behavior features including the time-of-day and location
distributions on both weekdays and weekends. For all Twitter users, the
aggregated travel behavior results also show that the time-of-day travel
patterns in Manhattan Island resemble that of the traffic flow; the
identification of OD pattern is also promising by comparing with the results of
travel survey.
