A New Traffic Prediction Method based on Dynamic Tensor Completion

Abstract

Traditional traffic prediction methods treat traffic data as one dimensional time series that can’t make full use of multi-mode correlation of traffic data, hence previous prediction models exist different levels of predictability and limits. To fully utilize the intrinsic multiple correlations of traffic data, in this paper a multi dimensional array-tensor model has proposed to encapsulate the traffic volume data. And a new traffic prediction method has been proposed, which includes rough estimation with intra-day trend and exact estimation with dynamic tensor completion (DTC) Experimental results demonstrate that the proposed prediction method is more accurate and reliable than traditional prediction methods.