Managing　multiple　traffic　modes　cooperatively　is　becoming　increasingly　important　owing　to　the　diversity　of　passenger　demands.　Short-term　passenger　flow　predictions　for　multi-traffic　modes　can　be　applied　to　the　management　of　the　multi-traffic　modes　system.　However,　this　is　challenging　because　the　spatiotemporal　features　of　multi-traffic　modes　are　complex.　Moreover,　the　passenger　flows　of　the　multi-traffic　modes　differentiated　and　fluctuated　significantly.　To　address　these　is-sues,　this　study　proposes　a　multitask　learning-based　model,　called　Res-Transformer,　for　short-term　inflow　prediction　of　multi-traffic　modes.　The　Res-Transformer　consists　of　two　parts:　(1)　modified　Transformer　layers　comprising　the　Conv-Transformer　layer　and　the　multi-head　attention　mechanism,　which　helps　extract　the　spatiotemporal　features　of　multi-traffic　modes,　and　(2)　the　structure　of　the　residual　network,　which　is　utilized　to　obtain　correlations　among　multi-traffic　modes　and　prevent　gradient　vanishing　and　explosion.　The　proposed　model　was　evaluated　using　two　large-scale　real-world　datasets　from　Beijing,　China.　One　was　a　traffic　hub,　and　the　other　was　a　residential　area.　The　results　not　only　demonstrate　the　effectiveness　and　robustness　of　the　Res-Transformer　but　also　prove　the　benefits　of　considering　multi-traffic　modes　jointly.　This　study　provides　critical　insights　into　short-term　inflow　prediction　of　the　multi-traffic　modes　system.