With　the　occurrence　of　large-scale　human　trajectories,　which　imply　spatial　and　temporal　patterns,　the　subject　of　mobility　prediction　has　been　widely　studied.　A　number　of　approaches　are　proposed　to　predict　the　next　location　of　a　user.　In　this　paper,　we　expect　to　lengthen　the　temporal　dimension　of　prediction　results　beyond　one　hop.　To　predict　the　future　locations　of　a　user　at　every　time　unit　within　a　specified　time,　we　propose　a　Markov-based　multi-hop　mobility　prediction　(Markov-MHMP)　algorithm.　It　is　a　hybrid　approach　that　considers　multiple　factors　including　personal　habit,　weekday　similarity,　and　collective　behavior.　On　a　GPS　dataset,　our　approach　performs　prediction　better　than　baseline　and　state-of-the-art　approaches　under　several　evaluation　criteria.