Passenger-flow　anomaly　detection　and　prediction　are　essential　tasks　for　intelligent　operation　of　the　metro　system.　Accurate　passenger-flow　representation　is　the　foundation　of　them.　However,　spatiotemporal　dependencies,　complex　dynamic　changes,　and　anomalies　of　passenger-flow　data　bring　great　challenges　to　data　representation.　Taking　advantage　of　the　time-varying　characteristics　of　data,　we　propose　a　novel　passenger-flow　representation　model　based　on　low-rank　dynamic　mode　decomposition　(DMD),　which　also　integrates　the　global　low-rank　nature　and　sparsity　to　explore　the　spatiotemporal　consistency　of　data　and　depict　abrupt　data,　respectively.　The　model　can　detect　anomalies　and　predict　short-term　passenger　flow　conveniently　and　flexibly.　For　anomaly　detection,　we　further　introduce　a　strong　temporal　Toeplitz　regularization　to　characterize　the　temporal　periodic　change　of　data,　so　as　to　more　accurately　detect　anomalies.　We　conduct　experiments　with　smart　card　transaction　data　from　the　Beijing　metro　system　to　assess　the　performance　of　the　model　in　two　use　cases.　In　terms　of　anomaly　detection,　the　experimental　results　demonstrate　that　our　method　can　detect　anomalies　efficiently,　especially　for　time　sequence　anomalies.　As　for　short-term　prediction,　our　model　is　superior　to　other　methods　in　most　cases.