Affiliations: [a]
College of Science, Chongqing University of Posts and Telecommunications, Chongqing, China
| [b]
School of Science, Wuhan University of Technology, Wuhan, China
Correspondence:
[*]
Corresponding author. Xinping Xiao, School of Science, Wuhan University of Technology, Wuhan 430070, China. E-mail: [email protected].
Abstract: The modeling and prediction of short-term traffic flow can reflect the prediction results of the traffic state and traffic flow data. In this paper, first, we use a high-dimensional tensor to represent the multi-mode characteristics of traffic flow data, and we make use of the basic operations properties of tensors, such as Tucker decomposition, to study the methods for filling in data, such as ITRM. Additionally, we preprocess the lost traffic flow and abnormal data. At the same time, we study the short-term traffic flow based on the “week-day-time” multi-mode of the traffic flow data. Using the grey model (GM (1, 1)) to predict the same period of the weekly mode, the scrolling grey model (SGM) of the same time period is predicted. For the time mode, a neural network time series of wavelet analysis is used to predict the traffic flow forecast during the same period. Then, the prediction results of the three different models are weighted by the grey correlation analysis method, and then, the coupling prediction model of the three models is obtained. In the end, according to the traffic flow data of the main road of Shaoshan road in Changsha, Hunan, China, we first preprocess the lost data by using the filling algorithm for the tensor data, and then, we make the traffic flow data complete, use the three tensor data modes of traffic flow, and analyze the results. The experimental results show that the coupling prediction model with the tensor model is much better than the single GM (1, 1) model, the SGM and the neural network prediction model.
