[论文解读] Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Zhiyong Cui, Ruimin Ke+2 more|arXiv (Cornell University)|Jan 7, 2018

Traffic Prediction and Management Techniques参考文献 44被引用 397

一句话总结

该论文提出一个深度堆叠的双向与单向 LSTM（SBU-LSTM）架构，用于预测全网络的交通速度，利用双向时间依赖和掩码机制来处理缺失数据。

ABSTRACT

Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed. A bidirectional LSTM (BDLSM) layer is exploited to capture spatial features and bidirectional temporal dependencies from historical data. To the best of our knowledge, this is the first time that BDLSTMs have been applied as building blocks for a deep architecture model to measure the backward dependency of traffic data for prediction. The proposed model can handle missing values in input data by using a masking mechanism. Further, this scalable model can predict traffic speed for both freeway and complex urban traffic networks. Comparisons with other classical and state-of-the-art models indicate that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.

研究动机与目标

• 在大规模网络和复杂城市环境中推动交通预测的深度学习应用。
• 开发能够在交通数据中捕捉前向和后向时间依赖的神经网络结构。
• 通过掩码机制实现即使输入值缺失也能稳健预测。
• 展示模型对网络级预测及不同交通网络类型的可扩展性。

提出的方法

• 为交通速度预测引入深度堆叠的双向与单向 LSTM（SBU-LSTM）架构。
• 使用 BDLSM 层来捕捉来自历史数据的空间特征和双向时间依赖。
• 在训练和推理过程中引入掩码机制以处理缺失的输入值。
• 将模型应用于高速公路和复杂城市交通网络。
• 与经典与最前沿模型进行比较，以评估准确性和鲁棒性。

实验结果

研究问题

• RQ1 基于深度 BD-LSTM 的架构是否能在网络级交通速度预测中优于传统模型？
• RQ2 将双向时间依赖与单向层结合是否能提升预测准确性和鲁棒性？
• RQ3 在交通速度输入中，掩码如何有效处理缺失数据？
• RQ4 所提模型是否具备扩展到涵盖高速公路和城市场景的大规模网络的能力？

主要发现

• SBU-LSTM 架构在整个交通网络的预测性能方面相较参考模型在准确性与鲁棒性上具有优越性。
• 双向时间依赖有助于捕捉交通速度数据中的回溯影响。
• 掩码机制使模型在训练和预测阶段能够有效处理缺失的输入值。
• 该方法可应用于高速公路和复杂城市交通网络。
• 深度架构展现出比传统方法更好的性能和韧性。