Information　and　communication　technology　development　has　yielded　large-scale　spatiotemporal　datasets,　such　as　mobile　phone,　automatic　collection　system,　and　car-hailing　data,　which　have　resulted　in　new　opportunities　to　investigate　urban　transportation　systems.　However,　few　studies　have　focused　on　regional　mobility　patterns.　This　study　presents　a　multistep　method　for　exploring　traffic　analysis　zone　(TAZ)-based　mobility　patterns　and　the　corresponding　relations　with　local　land　use　characteristics.　Based　on　a　large-scale　mobile　phone　dataset　from　a　major　mobile　phone　operator　in　Beijing,　we　applied　the　K-means　clustering　algorithm　to　the　hourly　aggregated　trip　data　to　create　clusters　of　TAZs　with　similar　temporal　mobility　patterns.　Land　use　characteristics　were　then　derived　and　correlated　with　the　temporal　TAZ-based　mobility　patterns.　Four　clusters　of　TAZs　with　the　similar　patterns　and　intensities　of　urban　activities　during　given　time　windows　were　identified.　Land　use　indicators,　such　as　residence　and　commercial　and　business　area　indicators,　were　correlated　with　specific　temporal　TAZ-based　mobility　patterns.　The　proposed　multistep　method　could　be　applied　in　other　cities　to　enrich　relevant　analyses　and　improve　urban　design　and　transportation　planning.