Road　traffic　state　estimation　is　an　essential　component　of　intelligent　transportation　systems　(ITSs).　However,　road　traffic　state　data　collected　by　traffic　detectors　are　often　incomplete,　which　can　cause　problems　across　a　variety　of　transportation　applications,　such　as　traffic　state　prediction　and　pattern　recognition.　We　present　GA-GAN　(Graph　Aggregate　Generative　Adversarial　Network),　consisting　of　graph　sample　and　aggregate　(GraphSAGE)　and　a　generative　adversarial　network　(GAN),　to　impute　missing　road　traffic　state　data.　Instead　of　using　the　original　road　network　structure,　which　presents　the　spatial　information　to　process　a　graph　operation,　we　reconstruct　the　road　network　according　to　the　correlation　coefficients　of　road　historical　data.　We　utilize　GraphSAGE　to　aggregate　the　temporal-spatial　information　from　the　neighbors　of　each　road　in　the　reconstructed　road　network.　GAN　is　used　to　generate　complete　traffic　state　data　from　the　extracted　temporal-spatial　information　to　achieve　traffic　state　data　imputation.　To　illustrate　the　efficient　performance　of　the　model,　experiments　are　conducted　on　traffic　data　collected　from　California　and　Seattle,　Washington,　showing　that　the　proposed　model　outperforms　state-of-the-art　methods.