Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation

Intelligent Transportation Systems (ITSs) are designed to alleviate traffic congestion and provide convenience for travelers or decision-makers. However, the challenge lies in obtaining complete and accurate traffic data because of various factors. Therefore, many models have been studied for traffic data imputation. Most studies overlook the extreme case where the missing rate of observed traffic data exceeds 90%, causing the existing traffic data imputation models to struggle in achieving satisfactory results. To address this extreme case, this paper proposes a novel Latent Factor Analysis model with Temporal Regularized Constraint (LFA-TRC) for handling road traffic data imputation. The proposed model is built on three main parts: a) Enhancing the stability of the training process through spatio-temporal linear biases, b) Capturing information in the traffic data time series by the first-order temporal difference constraint, and c) Accelerating convergence during the training process by incorporating generalized momentum. Experimental results on seven real-world traffic datasets demonstrate that the proposed LFA-TRC model outperforms eight state-of-the-art models in traffic data imputation and reduces the Mean Absolute Percentage Error (MAPE) by an average of 20.81%.