Graph　convolutional　networks　(GCNs)　are　widely　used　in　social　computation　such　as　urban　traffic　prediction.　However,　when　faced　with　city-level　forecasting　challenges,　the　graph-based　deep　learning　methods　struggle　to　process　large-scale　multivariate　data　effectively.　To　address　the　challenges　of　limited　scalability,　a　traffic　prediction　framework　based　on　a　hypergraph　message　passing　network　(HMSG)　is　proposed　in　this　article.　The　model　represents　the　urban　transportation　network　with　hypergraph,　where　nodes　denote　transportation　hubs　and　hyperedges　represent　their　relationship　at　geographical　and　feature　level.　Compared　with　pairwise　edges,　hyperedges　are　more　scalable　and　flexible,　providing　a　more　descriptive　representation　of　traffic　information.　The　HMSG　algorithm　updates　node　and　hyperedge　features　in　two　steps,　facilitating　effective　and　efficient　integration　of　hidden　spatial　features　across　layers.　The　proposed　framework　is　evaluated　on　large-scale　historical　datasets　and　demonstrates　its　completion　of　city-scale　traffic　prediction　tasks.　The　results　also　show　that　it　matches　the　accuracy　of　existing　traffic　prediction　methods　on　small-scale　datasets.　This　validates　the　potential　of　the　traffic　prediction　model　based　on　the　HMSG　algorithm　for　intelligent　transportation　applications.