A Bayesian robust CP decomposition approach for missing traffic data imputation

The inevitable problem of missing data is ubiquitous in the real transportation system, which makes the data-driven intelligent transportation system suffer from incorrect response. We propose a Bayesian robust Candecomp/Parafac (CP) tensor decomposition (BRCP) approach to deal with missing data and outliers by integrating the general form of transportation system domain knowledge. Specifically, when the lower rank tensor captures the global information, the sparse tensor is added to capture the local information, which can robustly predict the distribution of missing items and under the fully Bayesian treatment, the effective variational reasoning can prevent the over fitting problem. Real and reliable traffic data sets are used to evaluate the performance of the model in two data missing scenarios, which the experimental results show that the proposed BRCP model achieves the best imputation accuracy and is better than the most advanced baseline (Bayesian Gaussian CP decomposition (BGCP), high accuracy low-rank tensor completion (HaLRTC) and SVD-combined tensor decomposition (STD)), even in the case of high missed detection rate, the model still has the best performance and robustness.