Low-Rank Tensor and Hybrid Smoothness Regularization-Based Approach for Traffic Data Imputation With Multimodal Missing

Spatiotemporal traffic data is vitally important in intelligent transportation systems, but traffic data values are inevitably missing due to malfunctions in sensing devices or transmission networks. This will cause trouble for the subsequent traffic flow prediction work. Low-rank tensor imputation methods have recently received increasing attention to traffic data imputation due to their ability to capture the spatial-temporal information in the way of multi-dimension. However, traffic data often encounters challenging multimodal missing scenarios, including random missing, fiber missing, and slice missing. Thus, only accounting for the global low-rankness of traffic data is less sufficient to recover missing data. Motivated by the local smoothness of traffic data, we introduce the hybrid total variation into spatiotemporal traffic data imputation framework. The proposed model consists of the tensor nuclear norm, the first-order and the second-order total variation, which presents the global low-rankness and local smoothness prior, respectively. We utilize the ADMM to effectively solve the proposed model and establish the theoretical guarantee. Numerous experiments on two real-world datasets under three multimodal missing scenarios indicate that we proposed method outperforms some state-of-the-art traffic data imputation methods, especially for fiber missing and slice missing.