The　paper　presents　a　novel　approach　using　Hierarchical　Dirichlet　Processes　(HDP)　integrated　with　K-means　clustering　to　analyze　public　transit　commuting　behaviors　using　smartcard　and　POI　data.　The　HDP,　an　unsupervised　model,　is　designed　to　discern　travel　activities,　however,　little　is　done　for　this　purpose.　Our　study　proposed　representing　each　trip　using　four　features　(duration,　date,　arrival　time,　and　station　type　classified　using　POI-data)　as　inputs　to　the　HDP　model,　which　outputs　the　identification　of　specific　activities　such　as　home,　work,　and　leisure.　A　comparison　to　other　methods　including　trip　frequency,　activity　duration,　and　Hidden　Markov　models　demonstrates　that　our　approach　offers　superior　fit,　as　evidenced　by　lower　perplexity　and　higher　similarity　metrics.　To　further　refine　the　classification　of　commuting　behaviors,　we　applied　a　two-step　clustering　algorithm　that　considers　features　such　as　regularity,　temporality,　and　spatiality,　resulting　in　the　identification　of　strong　and　weak　commuting　behavior　patterns.　This　classification　provides　urban　planners　with　insights　into　the　spatiotemporal　characteristics　of　travelers　in　urban　rail　transit　systems,　thereby　supporting　more　effective　urban　planning.　©　2025　Elsevier　Ltd