Real-time　passenger-flow　anomaly　detection　at　all　metro　stations　is　a　very　critical　task　for　advanced　Internet　management.　Robust　principal　component　analysis　(RPCA)　based　method　has　often　been　employed　for　anomaly　detection　task　of　multivariate　time　series　data.　However,　it　ignores　the　spatio-temporal　features　of　regular　passenger-flow　patterns,　resulting　in　a　decrease　in　the　accuracy　of　anomaly　detection.　In　this　paper,　RT-STRPCA　model　integrating　temporal　periodicity　and　spatial　similarity　is　proposed　to　address　the　above　issues.　RT-STRPCA　model　detects　anomalies　by　decomposing　the　observation　data　into　normal　component　and　anomaly　component.　The　spatio-temporal　constraints　are　taken　into　account　to　extract　anomalies　more　accurately.　The　real-time　anomaly　detection　are　realized　by　a　sliding　window.　The　performance　of　RT-STRPCA　model　is　evaluated　on　synthetic　datasets　and　real-world　datasets.　The　experimental　results　on　synthetic　datasets　demonstrate　that　the　method　achieves　more　accurate　real-time　anomaly　detection　than　baseline　approaches　and　the　result　on　real-world　datasets　verify　the　utility　and　effectiveness　of　the　proposed　method.