Accurately　predicting　traffic　conditions　is　vital　for　smart　city　development,　yet　it　remains　challenging　due　to　the　intricate　spatiotemporal　dependencies　in　road　networks.　Existing　works　often　propose　intra-mixing　deep　learning-based　prediction　models　for　individual　nodes　and　share　parameters　among　them　or　spatial　intermixing　deep　learning-based　models　for　traffic　predictions.　However,　these　approaches　may　neglect　essential　principles　of　information　exchange　in　traffic　flow　or　capture　useless　or　even　erroneous　spatiotemporal　dependencies.　To　address　these　limitations,　we　propose　a　Mixing　Spatio-Temporal　neural　network　(MiST)　for　enhancing　traffic　predictions.　In　MiST,　we　propose　(i)　a　temporal　encoder　that　embeds　the　traffic　data　along　with　periodic　features,　(ii)　a　spatial　encoder　that　embeds　the　positional　information　in　graph　and　hypergraph　spectral　domains,　as　well　as　spatial　node　identities,　and　(iii)　a　mixing　spatio-temporal　encoder　that　merges　the　diverse　features　provided　by　the　temporal　and　spatial　encoders.　Our　empirical　evaluations　on　real-world　traffic　prediction　tasks,　including　flow　and　speed　predictions,　validate　the　superiority　of　MiST,　underscoring　its　innovative　contribution　to　traffic　prediction　methodologies.　©　2024　Copyright　held　by　the　owner/author(s).