Traffic　prediction　methods　on　a　single-source　data　have　achieved　excellent　results　in　recent　years,　especially　the　Graph　Convolutional　Networks　(GCN)　based　models　with　spatio-temporal　dependency.　In　reality,　various　modes　of　urban　transportation　operate　simultaneously.　They　influence　and　complement　each　other　in　common　space-time　occasions,　constituting　the　transportation　system　dynamically.　Thus,　traffic　data　from　multiple　sources　is　ostensibly　heterogeneous,　but　internally　correlated.　The　typical　single　data　driven　models　are,　however,　not　universally　applicable　for　heterogeneous　traffic　data.　To　address　this　issue,　we　propose　a　Multi-task　Hypergraph　Convolutional　Neural　Network　(MT-HGCN)　for　the　multi-source　traffic　prediction　problem.　The　framework　consists　of　a　main　task　and　a　related　task.　Both　tasks　are　based　on　Hypergraph　Convolutional　Neural　Networks　(HGCN)　and　are　devoted　to　two　prediction　problems.　Furthermore,　the　tasks　are　bridged　by　a　feature　compress　unit,　which　models　the　correlation　and　shares　the　latent　feature　to　improve　the　performance　of　the　main　task.　The　node-level　forecasting　has　been　evaluated　on　historical　datasets　of　Beijing　to　verify　the　effectiveness　of　the　proposed　method.　Compared　with　the　state-of-the-arts,　the　superior　performance　of　the　proposed　method　can　be　obtained.