Tensor alternating least squares grey model and its application to short-term traffic flows

Abstract

Traffic flow data, as an important data source for the research and development of intelligent transportation systems, contain abundant multi-mode features. In this paper, a high-dimensional multi-mode tensor is used to represent traffic flow data. The Tucker tensor decomposition least squares algorithm is used to establish the tensor alternating least squares GM (1,1) model by combining the modelling mechanism of the grey classical model GM (1,1) with the algorithm, and the modelling steps are obtained. To demonstrate the effectiveness of the new model, first, the multi-mode traffic flow data are represented by the tensor model, and the correlation of the traffic flow data is analysed. Second, two short-term traffic flow prediction cases are analysed, and the results show that the performance of the GM (1, 1) model based on the tensor alternating least squares algorithm is obviously better than that of the other models. Finally, the original tensor data and the approximate tensor data during the peak period from 8:00 to 8:30 a.m. for six consecutive Mondays are selected as the experimental data, and the effect of the new model is much better than that of the GM (1,1) model of the original tensor data.