A traffic data interpolation method for IoT sensors based on spatio-temporal dependence

Abstract

Missing traffic data collected by IoT sensors is a common issue. Having complete traffic data can help people with their studies and work in real world. A spatio-temporal enhanced k nearest neighbor (ST-KNN) method is proposed in this paper to interpolate missing traffic data according to its corresponding spatio-temporal dependence. The proposed method is improved in three aspects: initially, localized data are involved in the computation, the distance metric formula is re-designed secondly, and the data regression model is improved. We conducted our experimental evaluations on publicly available real dataset, and the results are compared to those from existing state-of-the-art models. The results of our experiments show that the method proposed in this paper can effectively improve traffic data interpolation accuracy.