Real-world　traffic　prediction　is　challenging　and　requires　accuracy,　efficiency,　and　generalizability　for　applications.　Most　studies　used　two-step　data　imputation-prediction　procedures　or　recurrent　networks　to　perform　predictions　based　on　imputed　data,　resulting　in　error　accumulation.　Training　the　same　model　independently　for　different　missing　rates　and　missing　data　patterns　requires　high　computational　costs.　This　strategy　cannot　be　integrated　with　existing　methods,　causing　low　generalizability.　We　propose　a　multi-task　pretraining　and　fine-tuning　(MTPF)　approach　to　address　these　issues.　Specifically,　we　pretrain　an　encoder　and　decoders　with　different　missing　data　patterns　and　missing　rates　and　an　adversarial　noise　to　learn　the　correlations　between　different　scenarios　and　generate　robust　hidden　representations.　The　encoder　and　decoder　perform　fine-tuning　for　specific　missing　data　scenarios　after　pretraining.　Multiple　parallel　tasks,　including　prediction,　data　imputation,　contrastive　learning,　and　adaptive　graph　learning,　are　used　for　pretraining　to　enable　the　model　to　learn　the　hidden　representation　for　prediction　and　data　imputation　during　fine-tuning.　The　MTPF　is　compared　with　nine　baseline　models　for　prediction　and　five　baseline　models　for　data　imputation　using　two　real-world　datasets.　The　experimental　results　show　that　MTPF　outperforms　many　state-of-the-art　baseline　models,　has　a　shorter　training　time,　and　is　a　model-agnostic　off-the-shelf　plug-in　that　improves　baseline　model　performance.