As　a　critical　application　in　intelligent　transportation　systems,　traffic　state　prediction　still　faces　various　challenges,　such　as　unsatisfactory　capability　of　utilizing　multi-source　data　and　modeling　spatiotemporal　network　relevancies.　Therefore,　we　propose　a　trajectory-based　multi-task　multi-graph　convolutional　network　(Tr-MTMGN),　a　novel　spatiotemporal　deep　learning　framework　for　traffic　state　prediction　on　a　citywide　scale.　This　method　firstly　mines　the　underlying　information　from　vehicle　trajectories　and　designs　a　multi-graph　convolution　block　to　investigate　spatial　correlations.　Sequentially,　the　multi-head　self-attention　layer　is　integrated　into　the　multi-task　learning　framework　to　capture　the　temporal　dependencies　of　the　traffic　state.　The　proposed　model　was　evaluated　on　field　data　collected　in　Zhangzhou,　China,　and　demonstrated　superior　performance　when　compared　with　existing　state-of-the-art　baselines.