With　the　advancement　of　mobile　computing　technology　and　the　widespread　use　of　GPS-enabled　mobile　devices,　research　on　semantic　trajectories　has　attracted　a　lot　of　attentions　in　recent　years,　and　the　semantic　trajectory　pattern　mining　is　one　of　the　most　important　issues.　Most　existing　methods　discover　the　similar　behavior　of　moving　objects　through　the　analysis　of　sequences　of　stops.　However,　these　methods　have　not　considered　the　duration　of　staying　on　a　stop　which　affects　the　accuracy　to　distinguish　different　behavior　patterns.　In　order　to　solve　the　problem,　this　paper　proposes　a　novel　approach　for　discovering　common　behavior　using　staying　duration　on　semantic　trajectory　(DSTra)　which　can　easily　differentiate　trajectory　patterns.　DSTra　can　be　used　to　detect　the　group　that　has　similar　lifestyle,　habit　or　behavior　patterns.　Semantic　trajectory　patterns　of　each　moving　object　are　mined　firstly.　Then,　the　time-weight　based　pattern　similarity　measurement　is　designed.　After　that,　a　hierarchical　clustering　method　with　pruning　strategy　is　proposed,　where　each　cluster　represents　the　common　behavior　patterns　from　moving　objects.　Finally,　experiments　on　both　real-world　dataset　and　synthetic　dataset　demonstrate　the　effectiveness,　precision　and　efficiency　of　DSTra.　©　2017,　Science　Press.　All　right　reserved.