Accurate,　real-time,　and　efficient　traffic　data,　crucial　for　intelligent　transportation　systems,　which　is　often　disrupted　in　the　real　world　due　to　the　influence　of　weather,　interference,　and　equipment　failures.　Generative　Adversarial　Networks　(GANs),　have　achieved　significant　results　in　image　restoration,　providing　insights　for　traffic　data　imputation.　Recent　research　has　focused　on　developing　more　effective　and　reasonable　model　by　learning　transferable　common　knowledge　from　different　cities　or　different　scenarios,　which　is　also　an　excellent　idea　for　improving　the　data　imputation　performance.　In　light　of,　we　contrive　to　design　a　GANs　based　transferable　traffic　data　imputation　model,　namely　Multi　Domain　Generative　Adversarial　Transfer　Learning　Network　(MDTGAN).　Firstly,　the　model　consists　of　two　stages.　In　pre-training　stage,　the　model　utilizes　the　source　domain　datasets　(Similar　cities　road　network　datasets)　to　update　parameters　and　transferred　them.　Then,　the　model　parameters　are　optimized　using　the　target　domain　dataset　(Target　city　road　network　dataset)　in　fine-tuning　stage　to　avoid　the　issue　of　insufficient　samples　caused　by　data　missing.　Secondly,　to　adapt　the　model　to　different　dataset　topologies,　MDTGAN　models　the　node-level　data　by　taking　node　time　series　as　input,　enabling　the　model　to　autonomously　learn　the　prevalent　spatiotemporal　correlations　inherent　in　nodes.　Additionally,　we　introduce　a　domain　discriminator　module　that　guides　the　spatial　encoder　in　learning　domain-invariant　features　of　network　node　spatial　information,　enhancing　the　model　generalization　ability.　The　experiments　conducted　on　three　publicly　available　datasets,　in　which　one　dataset　is　regarded　as　the　target　dataset,　while　the　others　serve　as　source　datasets.　The　experimental　results　demonstrate　that　the　MDTGAN　model　consistently　outperforms　the　baseline　models.　Specifically,　the　MDTGAN　model　can　transfer　valuable　knowledge　from　the　source　domain　datasets　to　improve　data　imputation　performance　on　the　target　domain　dataset,　providing　practical　significance　for　cities　lacking　historical　traffic　data.