One　of　the　challenging　topics　in　Intelligent　Transportation　Systems　(ITSs)　is　the　metro　passenger　flow　prediction.　It　has　great　practical　significance　for　the　daily　crowd　management　and　vehicle　scheduling　of　metro　passenger　flow.　Recently,　Graph　Convolutional　Networks　(GCN)　represent　a　station　of　metros　by　aggregating　information　of　stations　directly　and　indirectly　connected　with　the　station,　and　improve　the　effectiveness　of　predicting　metro　passenger　flow.　Despite　its　effectiveness,　the　neighborhood　aggregation　scheme　also　brings　two　limitations　in　predicting　metro　passenger　flow.　First,　it　limits　to　predict　accurately　the　peak　of　passenger　flow　with　large　data　fluctuation.　Second,　it　enlarges　the　impact　of　noisy　data　and　makes　results　vulnerable　to　noisy　data　of　metro　passenger　flow.　To　solve　these　problems,　we　propose　regularized　spatial-temporal　graph　convolutional　networks　for　metro　passenger　flow　prediction　(PMR-GCN).　More　specifically,　we　first　propose　a　novel　personalized　enhanced　GCN　(P-GCN),　which　defines　a　trainable　diagonal　matrix　to　adaptively　learn　to　control　the　impact　of　the　neighborhood　aggregation　scheme　for　predicting　the　peak　of　passenger　flow.　Then,　we　introduce　the　multi-head　self-attention　mechanism　to　capture　richer　spatial-temporal　features　of　passenger　flow.　In　addition,　we　utilize　a　Fourier　Transform　module　and　a　dual　structure　based　on　KL-divergence　regularization　to　improve　the　robustness　of　the　proposed　model.　Extensive　experiments　on　Shanghai,　Chongqing,　and　Hangzhou　datasets　demonstrate　the　superiority　of　our　model　over　the　state-of-the-art　baseline　methods.　The　code　of　PMR-GCN　is　available　at　https://github.com/cgao-comp/PMC-GCN.　IEEE