Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
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DOI：
10.1016/j.trc.2020.102674
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发表时间：
2020-09-01
影响因子：
8.3
通讯作者：
Wang, Yinhai
中科院分区：
文献类型：
--
作者：
Cui, Zhiyong;Ke, Ruimin;Wang, Yinhai
基于深度学习方法，尤其是循环神经网络（RNN）的短期流量预测近年来备受关注。然而，基于RNN的模型在交通预测中的潜力在时空数据的预测能力和处理缺失数据的能力方面尚未得到充分发挥。在本文中，我们关注基于 RNN 的模型，并尝试重新制定将 RNN 及其变体纳入流量预测模型的方法。提出了一种堆叠式双向和单向 LSTM 网络架构（SBU-LSTM）来辅助设计用于交通状态预测的神经网络结构。作为该架构的关键组件，双向 LSTM (BDLSM) 用于捕获时空数据中的前向和后向时间依赖性。为了处理时空数据中的缺失值，我们还提出了一种 LSTM 结构（LSTM-I）中的数据插补机制，通过设计插补单元来推断缺失值并辅助流量预测。 LSTM-I 的双向版本被纳入 SBU-LSTM 架构中。使用两个真实世界的全网交通状态数据集进行实验并发布，以方便进一步的交通预测研究。评估多种类型的多层LSTM或BDLSTM模型的预测性能。实验结果表明，所提出的SBU-LSTM架构，特别是两层BDLSTM网络，可以在全网流量预测的准确性和鲁棒性方面实现优异的性能。此外，综合比较结果表明，当模型的输入数据包含不同模式的缺失值时，基于 RNN 的模型中所提出的数据插补机制可以实现出色的预测性能。
Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in terms of the predictive power of spatial-temporal data and the capability of handling missing data. In this paper, we focus on RNN-based models and attempt to reformulate the way to incorporate RNN and its variants into traffic prediction models. A stacked bidirectional and unidirectional LSTM network architecture (SBU-LSTM) is proposed to assist the design of neural network structures for traffic state forecasting. As a key component of the architecture, the bidirectional LSTM (BDLSM) is exploited to capture the forward and backward temporal dependencies in spatiotemporal data. To deal with missing values in spatial-temporal data, we also propose a data imputation mechanism in the LSTM structure (LSTM-I) by designing an imputation unit to infer missing values and assist traffic prediction. The bidirectional version of LSTM-I is incorporated in the SBU-LSTM architecture. Two real-world network-wide traffic state datasets are used to conduct experiments and published to facilitate further traffic prediction research. The prediction performance of multiple types of multi-layer LSTM or BDLSTM models is evaluated. Experimental results indicate that the proposed SBU-LSTM architecture, especially the two-layer BDLSTM network, can achieve superior performance for the network-wide traffic prediction in both accuracy and robustness. Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model's input data contains different patterns of missing values.