Graph　convolutional　neural　networks,　which　aim　to　learn　the　spatial　correlation　within　multivariate　time-series　data,　have　achieved　significant　advancements　in　traffic　prediction.　Despite　the　proliferation　of　various　spatiotemporal　graph　neural　networks,　a　fundamental　question　of　the　predefined　graphs　still　remain　to　be　explored:　how　can　the　graph　be　dynamically　optimized　as　time　shifts?　Especially　for　tasks　such　as　rail　transit　where　travel　patterns　of　passengers　change　periodically.　In　other　words,　the　spatial　correlation　of　multivariate　traffic　time-series　data　is　closely　intertwined　with　temporal　features,　which　are　tricky　to　reflect　through　static　graphs　with　handcrafted　association.　To　adaptively　learn　the　spatial　correlation　in　feature　domain,　we　propose　hierarchical　hypergraph　attention　networks　(HHGATs).　The　hierarchical　nature　of　the　framework　is　reflected　in　both　temporal　and　spatial　dimensions.　At　different　time　spans,　the　model　explores　the　hidden　hyperedges　with　a　local　hypergraph　attention　mechanism　and　optimizes　the　hypergraph　with　a　global　attention　mechanism.　The　nodelevel　prediction　results　are　then　fused　after　the　downstream　tasks　of　spatiotemporal　hypergraph　convolutional　networks.　The　effectiveness　and　accuracy　of　the　proposed　model　are　evaluated　on　historical　datasets　of　Beijing　and　Hangzhou.　Compared　to　the　baselines,　the　proposed　approach　consistently　demonstrates　superior　performance　in　various　evaluation　scenarios.　©　2020　IEEE.