This　study　aims　to　solve　the　problem　of　predicting　uncertain　trajectories　of　moving　objects,　including　mobile　devices,　vehicles,　airplanes,　and　hurricanes.　In　order　to　design　a　general　schema　of　trajectory　prediction　on　large-scale　moving　objects　data,　techniques　of　frequent　trajectory　patterns　mining　and　Gaussian　mixture　regression　model　are　employed,　and　a　multiple-motion-pattern　trajectory　prediction　model　is　proposed.　The　proposed　key　techniques　include:　1)　as　for　simple　motion　patterns,　a　new　trajectory　prediction　algorithm　based　on　frequent　trajectory　pattern　tree　(FTP-tree)　is　proposed,　which　employs　a　density　based　region-of-interest　discovery　approach　to　partition　a　large　number　of　trajectory　points　into　distinct　clusters.　Then,　it　generates　a　frequent　trajectory　pattern　tree　to　forecast　continuous　locations　of　moving　objects.　Experimental　results　show　that　the　FTP-tree　based　trajectory　prediction　algorithm　performs　better　than　existing　prediction　approaches　with　the　guarantee　of　time　efficiency.　2)　Gaussian　mixture　regression　approach　is　used　to　model　complex　multiple　motion　patterns,　which　calculates　the　probability　distribution　of　different　types　of　motion　patterns,　as　well　as　partitions　trajectory　data　into　distinct　components,　in　order　to　predict　the　most　possible　trajectories　of　moving　objects　via　Gaussian　process　regression.　Experimental　results　show　a　high　accuracy　and　low　time　consumption　on　trajectory　prediction,　as　compared　to　the　hidden　Markov　model　approach　and　the　Kalman　filter　one.　Copyright　©　2018　Acta　Automatica　Sinica.　All　rights　reserved.