ARCI-T: ANCHOR-RESIDUAL CONTROLLED IMPUTATION FOR MISSING TRAFFIC SENSOR DATA

Missing values in traffc sensor records may manifest as isolated points, consecutive blocks, or sensor-level outages, yielding heterogeneous temporal and spatial evidence across different missing positions. Deterministic recovery provides a stable reference, while learned models can capture richer spatiotemporal dynamics. Rather than treating these two solutions as competing alternatives, we formulate traffc imputation as controlled correction around a retained deterministic reference. We formalize Anchor-Residual Controlled Imputation for Missing Traffc Sensor Data (ARCI-T), which employs the deterministic recovery as an explicit Anchor, learns an anchor-relative correction, and determines the contribution magnitude of this correction at each missing position based on available observational evidence. This design preserves the deterministic reference as a fallback while enabling learned corrections to adapt under heterogeneous missingness conditions. Across four traffc benchmarks under random and structured missingness protocols, ARCI-T achieves state-of-the-art or highly competitive imputation accuracy, demonstrating that controlled correction is an effective approach for traffic sensor imputation.