In　recent　years,　with　the　acceleration　of　the　urbanization　process,　the　contradiction　between　transportation　infrastructure　facilities　and　spatial　layout　of　urban　land　use　is　escalating.　However,　integrated　urban　planning　and　transportation　planning　provides　a　new　way　to　significantly　relieve　traffic　congestion　and　the　phenomenon　of　jobs-housing　separation　brought　by　urbanization.　In　this　study,　the　massive　internet　data　of　urban　planning　(e.g.　internet-based　positioning　data)　and　traditional　data　resources　of　the　traffic　system　(e.g.　smart　card　data)　are　combined　to　identify　the　jobs-housing　relationship　around　urban　rail　transit　stations　instead　of　the　method　of　theoretical　modeling.　All　the　stations　are　classified　into　different　categories　on　this　basis.　Besides,　as　for　the　connections　of　different　regions　and　the　spatial　distribution　of　the　urban　population,　the　method　of　smart　card　data　mining　is　adopted　to　analyze　commuting　characteristics　and　spatial　distribution　of　origin-destination　travel　demand　for　different　categories　of　stations.　Finally,　analysis　of　the　correlation　between　the　resident　population　around　urban　rail　transit　stations　and　commuting　passenger　flow　is　carried　out　by　statistical　methods.　Corresponding　results　can　be　further　applied　to　the　integrated　planning　of　transportation　and　land　use.