Different　individuals　may　move　to　different　regions　over　time,　but　every　individual　has　several　fixed　travel　positions　or　unique　travel　patterns.　Predicting　destinations　of　each　individual　facilitates　traffic　demand　management,　which　has　great　research　value.　Based　on　the　data　of　multi-day　GPS　and　passengers＇　travel　survey,　a　hidden　Markov　model　(HMM)　is　employed　in　this　paper　to　predict　trip　destination　for　weekdays　and　weekends.　Firstly,　the　habit　of　destination　choice　among　consecutive　days　and　weeks　can　be　discovered　by　identifying　frequently　visited　destinations.　Then,　on　the　basis　of　Viterbi　algorithm,　this　paper　takes　frequently　visited　destinations　as　one　of　the　factors　of　the　predicting　process　and　constructs　a　travel　destination　prediction　model　based　on　HMM.　Then,　the　HMM　is　calibrated　with　Baum-Welch　algorithm　and　passengers＇　travel　destination　characteristics　are　effectively　analyzed.　Finally,　the　HMM　was　compared　with　several　classical　algorithms.　The　results　show　that　the　place　of　residence　and　work　are　the　most　probable　activities　to　occur　and　workplace　dominates　the　activities　when　duration　is　longer　than　8　h.　Moreover,　the　results　of　frequently　visited　destinations　identification　indicate　that　the　patterns　of　destination　choice　on　weekdays　and　weekends　are　different　from　each　other.　In　addition,　the　results　show　that　the　prediction　accuracy　on　weekdays　is　higher　than　that　on　weekends　and　HMM　outperforms　other　prevailing　algorithms.　The　method　proposed　in　this　paper　can　be　applied　to　real-time　travel　navigation　applications,　as　well　as　supporting　health　and　safety　fields,　such　as　epidemic　prevention　and　control.