A　Time　Space　Diagram　(TSD)　plays　an　important　role　in　transportation　research　and　practice　due　to　its　capability　to　exhibit　traffic　dynamics　in　time　and　space.　Based　on　TSDs,　this　paper　aims　to　reconstruct　the　traffic　spatio-temporal　state　with　the　aid　of　Generative　Adversarial　Networks　(GANs).　By　mining　traffic　state　correlations　and　traffic　pattern　similarities　between　lanes　with　or　without　sufficient　observations,　the　proposed　Traffic　State　Reconstruction　GAN　(TSR-GAN)　model　can　well　estimate　the　traffic　states　for　road　segments　with　a　strong　learning　capability.　Specifically,　the　traffic　states　of　lanes　are　converted　to　TSDs,　in　which　the　color　represents　the　values　of　traffic　variables　(e.g.,　speed　or　density).　The　TSDs　of　lanes　with　or　without　sufficient　data　are　utilized　to　train　the　proposed　TSR-GAN　model.　The　fine-tuned　TSR-GAN　model　reconstructs　traffic　states　for　road　segments　with　deficient　sensor　coverage　by　restoring　the　high-resolution　TSD　from　its　low-resolution　observation.　With　trajectory　datasets　from　Next　Generation　Simulation　(NGSIM),　this　paper　verifies　the　performance　of　the　TSR-GAN　model　by　estimating　travel　time　via　the　reconstructed　TSDs.　Numerical　results　demonstrate　that　the　proposed　model　possesses　a　desirable　generalization　and　transferability,　demonstrating　the　promise　of　reconstructing　traffic　states　under　various　conditions.　(c)　2021　Elsevier　B.V.　All　rights　reserved.