A Low Rank Dynamic Mode Decomposition Model for Short-Term Traffic Flow Prediction

Traffic flow data has three main characteristics: large amount of noise and incompleteness, temporal and spatial correlation, and dynamic sequential property. Problems of noise, loss and incompleteness could decrease the prediction performance and make it difficult for transportation system management. Inspired by recent work on low rank representation (LRR) and dynamic mode decomposition (DMD), we propose a Low Rank Dynamic Mode Decomposition (LRDMD) model which solves the aforementioned problems simultaneously. LRDMD predicts traffic flow by using a state transition matrix which characterizes the relationship between temporally neighboring fragments of traffic flow with low rank regularization. We conduct experiments of traffic flow prediction of different time intervals using loop coil detector data of Qingdao, and the results show that LRDMD outperforms state-of-the-art methods.