A Novel Completion Method for Sparse Traffic Data Imputation

Traffic data imputation is essential in smart cities and the Internet of Things (IoT). Tensor completion is an efficient method for traffic data imputation. However, these methods overlook the integration of contextual and spatial information, which are important for traffic data imputation. Hence, this study proposes a novel tensor completion method leveraging contextual and spatial information for sparse traffic data imputation (STDI). Initially, we develop a model for STDI, treating traffic data as tensors and applying tensor completion for imputing missing values. Then, to account for contextual information, we compute the contextual scores of roads and reorganize the road indices according to the scores. Additionally, we utilize the Laplacian matrix to reveal spatial information and optimize the objective function to enhance imputation accuracy. Finally, we design a parallel algorithm for STDI on GPU for efficient computation. Extensive experiments demonstrate that the proposed method is superior to existing methods.