Traffic　flow　prediction　in　a　given　area　is　often　influenced　by　the　interactions　with　complex　dependencies　among　multiple　areas.　By　far,　it　remains　unexplored　to　obtain　interactive　information.　To　address　the　issue,　MSTMN　was　proposed,　a　multi-task　learning　framework　that　jointly　learns　interactive　information　and　spatiotemporal　dependencies　across　tasks.　MSTMN　consists　of　a　node　network,　an　edge　network,　and　a　prediction　network.　The　node　network　and　edge　network　were　trained　using　the　proposed　meta-fully　convolutional　blocks　to　extract　interactive　features　and　generalizable　features.　The　prediction　network　employed　the　meta-gated　fusion　and　the　recalibration　block　to　both　integrate　these　learned　features　and　external　factors.　This　ensures　that　the　features　capture　optimal　interaction　information　during　the　training　phase.　The　proposed　model　was　validated　on　two　real-world　movement-on-demand　traffic　datasets　collected　in　Xiamen,　China.　Experimental　results　showed　that　MSTMN　improved　performance　by　38.42%　and　31.77%　for　one-step　and　multi-step　prediction　compared　to　the　state-of-the-art　baseline.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.