Tensor-Based Recurrent Neural Network and Multi-Modal Prediction With Its Applications in Traffic Network Management

Predicting the future traffic flows by applying deep learning methods has become an alternative way for transportation network management. Combining recurrent neural networks (RNNs) with tensor to implement accurate predictions has drawn intensive attention. However, traditional RNNs cannot deal with the high-order traffic flow data and capture their inherent structural relationship to provide accurate multi-modal prediction services. Therefore, this article focuses on proposing a series of tensor-based RNNs (T-RNNs) and a T-RNNs based multi-modal prediction approach (TMMP) to provide accurate prediction services. First, we propose three types of T-RNNs including tensor-based vanilla RNN, tensor-based long short-term memory (T-LSTM) and tensor-based gated recurrent unit (T-GRU), in which the input, output and weights are arbitrary high-order tensors. Then, to compress the weight parameters, we further propose two compact TT-based GRU (TT-GRU) and Tucker-based GRU (Tucker-GRU) models by applying tensor train (TT) and Tucker decompositions to T-GRU model. Afterwards, based on the high-order output tensor generated by T-RNNs, a TMMP approach is proposed to achieve the accurate predictions under various scenarios. Extensive experimental results on the metro traffic flow dataset demonstrate that the proposed TMMP approach can improve the traffic flow prediction accuracy by at most 25.29 percentage compared with the traditional MSE-based approaches. Meanwhile, compared with the T-GRU model, the TT-GRU model can compress the number of parameters by 200~780 times. The proposed T-RNNs and TMMP approach can adapt to different application scenarios and can be used to improve the efficiency of transportation management.