Traffic　forecasting　is　one　of　the　important　functions　of　intelligent　transportation　systems　(ITS),　which　is　of　great　significance　to　user　experience　and　urban　traffic　control.　Edge　computing,　as　an　emerging　technology,　has　been　a　promising　candidate　for　real-time　and　accurate　traffic　flow　prediction.　In　this　paper,　we　propose　an　adversarial　domain　adaptation　model　for　traffic　forecasting　in　an　edge　computing　system.　We　design　the　feature　mapping　and　the　discriminator　optimization　objective　functions.　The　traffic　features　of　target　domain　can　be　aligned　with　the　traffic　features　of　source　domain　by　adversarial　domain　adaptation　training.　We　utilize　the　proposed　model　for　different　traffic　prediction　tasks　such　as　traffic　flow　and　occupancy.　We　also　present　extensive　simulations　by　using　real-world　traffic　dataset.　We　show　that　the　proposed　model　can　achieve　better　prediction　accuracy　than　the　other　algorithms　when　the　number　of　training　dataset　is　insufficient.　©　2021　IEEE.