A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction
Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing observations and anomalous disturbances make this task challenging, particularly when prediction relies on a single traffic view. This paper proposes a Multi-View Coupled Tensor Decomposition (MVCTD) model for online traffic prediction from imperfect multi-view observations, such as speed, flow, and occupancy. The proposed model uses coupled tensor decomposition to build a structured latent forecasting space, in which shared spatial structures across traffic views and view-specific temporal dynamics are jointly...
| Authors | Quan Yu, Jie Ni, Yu-Hong Dai et al. |
|---|---|
| Primary category | math.OC |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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