Multitask Neural Tensor Factorization for Road Traffic Speed-Volume Correlation Pattern Learning and Joint Imputation

Missing data is a common and critical problem in the stage of traffic data collection and processing. How to impute the missing values in spatio-temporal traffic data has been a challenging topic for a long time. Recently, a variety of methods have been proposed to impute the missing values. Among them, the tensor-based methods show higher competence in multi-dimensional traffic data imputation. However, the previous studies of tensor factorization rarely considered the joint imputation of multiple correlative data such as traffic speed and traffic volume. In this paper, a novel method called Multi-Task Neural Tensor Factorization (MTNTF) is proposed to learn the non-linear correlation patterns of traffic speed-volume, and then address the joint imputation of traffic speed and traffic volume. Extensive experiments on a real dataset show our MTNTF significantly outperforms the state-of-the-art methods in element-wise missing and fiber-wise missing cases. In addition, our method can impute the slice-wise missing values of traffic volume based on incomplete traffic speed.

Automated summary

Missing data is a common and critical problem in traffic data collection and processing. Tensor-based methods have been effective in imputing missing values in spatio-temporal traffic data. However, most previous studies did not consider the joint imputation of multiple correlated data such as traffic speed and volume. This paper proposes a novel method called Multi-Task Neural Tensor Factorization (MTNTF) to address the joint imputation of traffic speed and volume, and experiments demonstrate its superiority over state-of-the-art methods.