High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data with L_p,ϵ-Norm

In the realm of intelligent transportation systems (ITS), achieving optimal system performance relies heavily on the acquisition of comprehensive and high-quality spatio-temporal traffic data. In practical data-gathering processes, factors such as sensor malfunctions or communication interruptions often lead to incomplete or missing data records, which in turn substantially hinder the advancement of ITS applications. To address missing spatio-temporal data, a widely adopted paradigm involves the Latent Factorization of Tensors (LFT) model. Traditional LFT frameworks often employ the standard L₂ metric in their learning objective, making them easily affected by abnormal data points. Moreover, impulse noise frequently arises in sensors and communication scenarios. To address these limitations, this paper develops an Adaptive L_p,ϵ-norm-incorporated Latent Factorization of Tensors (L_p,ϵLFT) model founded on two-fold concepts: (a) constructing a generalized objective function grounded in the L_p,ϵ-norm distance to enhance robustness against outliers; (b) realizing the self-adaptation of model hyper-parameters through a fuzzy controller to enhance model practicality. Experimental evaluations on six traffic speed datasets derived from multiple metropolitan traffic networks demonstrate that the proposed L_p,ϵLFT model yields significantly higher imputation accuracy and superior computational efficiency compared with seven state-of-the-art approaches.