Tensor-Train Fuzzy Deep Computation Model for Citywide Traffic Flow Prediction

Abstract

Accuracy is extensively considered a key issue for traffic big data prediction in a vehicular cyber-physical system (VCPS). Deep learning with super performance has been successfully applied to traffic prediction for feature learning. However, uncertain traffic big data pose a remarkable challenge on current deep learning models, which work in a vector space in a deterministic manner and fail to learn the features of uncertain traffic data. This study solves the problem of citywide traffic flow prediction to satisfy the accuracy requirement of the VCPS from the perspective of users. In this study, a tensor-train fuzzy deep convolution (TFDC) approach is first proposed to satisfy the accuracy requirements of traffic flow prediction. Moreover, the Tucker deep computation (T-TFDC) approach for TFDC is proposed to satisfy the prediction accuracy requirements with low computational complexity. The TFDC model is built upon the fuzzy deep convolutional network, which uses unified tensor data representation for spatio-temporal traffic flow data. The key idea of the T-TFDC is to introduce Tucker decomposition into the TFDC model to compress parameters for traffic flow feature learning. Furthermore, learning algorithms for training TFDC and T-TFDC model parameters are devised on the basis of the back-propagation strategy. Experimental results on the TaxiBJ and BikeNYC data sets verify the effectiveness and efficiency of the proposed approaches over state-of-the-art methods.