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.

中文翻译：

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基于张量的递归神经网络和多模态预测及其在交通网络管理中的应用

通过应用深度学习方法预测未来的交通流量，已成为交通网络管理的另一种方法。将递归神经网络（RNN）与张量相结合以实现准确的预测已经引起了广泛的关注。但是，传统的RNN无法处理高阶交通流数据，也无法捕获其固有的结构关系以提供准确的多模式预测服务。因此，本文着重于提出一系列基于张量的RNN（T-RNN）和基于T-RNNs的多模态预测方法（TMMP），以提供准确的预测服务。首先，我们提出三种类型的T-RNN，包括基于张量的香草RNN，基于张量的长期短期记忆（T-LSTM）和基于张量的门控递归单元（T-GRU），其中输入 输出和权重是任意的高阶张量。然后，为了压缩权重参数，我们通过将张量列（TT）和Tucker分解应用于T-GRU模型，进一步提出了两个紧凑的基于TT的GRU（TT-GRU）和基于Tucker的GRU（Tucker-GRU）模型。然后，基于T-RNN生成的高阶输出张量，提出了一种TMMP方法来实现各种情况下的准确预测。在地铁交通流数据集上的大量实验结果表明，与传统的基于MSE的方法相比，所提出的TMMP方法可以将交通流的预测精度提高最多25.29％。同时，与T-GRU模型相比，TT-GRU模型可以将参数数量压缩200〜780倍。