The　widespread　deployment　of　road　sensors　in　the　Internet　of　Things　(IoT)　allows　for　fine-grained　data　integration,　which　is　a　fundamental　demand　for　data-driven　applications.　Sensing　data　with　inevitable　missing　and　substantial　anomalies　are　unavoidable,　due　to　unstable　network　communication,　faulty　sensors,　etc.　Recent　tensor　completion　studies　have　demonstrated　the　superiority　of　deep　learning　in　imputation　tasks　by　precisely　capturing　the　intricate　spatiotemporal　dependencies/correlations.　However,　ignoring　the　significance　of　initial　interpolation　in　these　methods　results　in　unstable　performance,　especially　for　complicated　missing　scenarios　across　large-scale　data.　Additionally,　the　existing　interpolation　methods　utilize　recursive　signal　propagation　along　spatiotemporal　dimensions,　which　produce　noise　accumulation　where　the　dependencies　are　uncorrelated.　In　this　study,　we　design　a　multiattention　tensor　completion　network　(MATCN)　for　modeling　multidimensional　representation　in　the　presence　of　missing　entries.　MATCN　sparsely　sampled　historical　fragments　and　utilized　a　gated　diffusion　convolution　layer　to　generate　the　initial　schemes,　which　mitigate　the　exposure　bias　existing　in　previous　traffic　imputation　models.　In　addition,　we　develop　a　spatial　signal　propagation　module　and　a　temporal　self-attention　module　as　the　basic　stack　block　of　deep　networks,　which　executes　representation　aggregation　and　dynamic　dependencies　extraction　at　the　spatiotemporal　level.　This　architecture　empowers　MATCN　with　progressive　completion　capacities　for　complex　data　missing　scenarios.　Numerical　experiments　on　four　real-world　traffic　data　sets　with　various　missing　scenarios　demonstrate　the　superiority　of　MATCN　over　multiple　state-of-the-art　imputation　baselines.