While　the　utilization　of　transportation　systems　is　on　the　rise,　significant　data　quality　concerns　persist,　including　data　loss　and　noise　arising　from　network　transmission　delays　and　detector　malfunctions.　Various　methods　for　data　imputation　exist,　among　which　diffusion-based　approaches　have　demonstrated　competitive　outcomes.　Nonetheless,　diffusion　models,　primarily　employed　in　matrix-structured　data　like　images,　fail　to　fully　exploit　the　inherent　graph　structure　of　traffic　data.　To　enhance　the　quality　of　data　filling,　we　propose　a　novel　method　that　combines　spatio-temporal　transformer　and　a　conditional　diffusion　model　(STCDM).　The　introduction　of　the　conditional　diffusion　model　involves　using　observable　traffic　data　as　conditional　information　in　the　reverse　process,　allowing　it　to　learn　the　underlying　probability　distribution　and　guide　the　generation　of　high-quality　data　samples.　The　spatio-temporal　transformer　module　is　selected　as　the　basic　denoising　function,　capturing　comprehensive　spatio-temporal　context　information　of　traffic　data.　Our　experimental　results,　conducted　on　public　transportation　datasets　with　various　missing　patterns　and　rates,　indicate　that　STCDM　exhibits　superior　performance　by　achieving　up　to　a　1.11%　improvement　over　the　second-ranked　conditional　score-based　diffusion　model　across　popular　performance　metrics.