Accurate prediction of traffic status in real time is critical for advanced traffic management and travel navigation guidance. There are many attempts to predict short-term traffic flows using various deep learning algorithms. Most existing prediction models are only tested on spatiotemporal data assuming no missing data entries. However, this ideal situation rarely exists in real world due to sensor or network transmission failure. Missing data is a nonnegligible problem. Previous studies either remove time series with missing entries or impute missing data before building prediction models. The former may cause insufficient data for model training, while the latter adds extra computational burden and the imputation accuracy has direct impacts on the prediction performance. In this study, we propose an online framework that can make spatiotemporal predictions based on raw incomplete data and impute possible missing values at the same time. We design a novel spatial and temporal regularized matrix factorization model, namely LSTM-GL-ReMF, as the key component of the framework. The Long Short-term Memory (LSTM) model is chosen as the temporal regularizer to capture temporal dependency in time series data and the Graph Laplacian (GL) serves as the spatial regularizer to utilize spatial correlations among network sensors to enhance prediction and imputation performance. The proposed framework integrating with the LSTM-GL-ReMF model are tested and compared with other state-of-the-art matrix factorization models and deep learning models on three uni-variate and multi-variate spatiotemporal traffic datasets. The experimental results show our approach has a robust and accurate performance in terms of prediction and imputation accuracy under various data missing scenarios.

实时准确预测交通状况对于高级交通管理和旅行导航指导至关重要。有许多尝试使用各种深度学习算法来预测短期交通流量。大多数现有的预测模型仅在假设没有丢失数据条目的情况下对时空数据进行测试。然而，由于传感器或网络传输故障，这种理想情况在现实世界中很少存在。丢失数据是一个不可忽视的问题。以前的研究要么在构建预测模型之前删除具有缺失条目的时间序列或估算缺失数据。前者可能导致模型训练数据不足，而后者增加了额外的计算负担，插补精度直接影响预测性能。在这项研究中，我们提出了一个在线框架，可以根据原始不完整数据进行时空预测，同时估算可能的缺失值。我们设计了一个新颖的空间和时间正则化矩阵分解模型，即 LSTM-GL-ReMF，作为框架的关键组件。选择长短期记忆 (LSTM) 模型作为时间正则化器来捕获时间序列数据中的时间依赖性，而拉普拉斯图 (GL) 作为空间正则化器，利用网络传感器之间的空间相关性来增强预测和插补性能。所提出的与 LSTM-GL-ReMF 模型集成的框架在三个单变量和多变量时空交通数据集上进行了测试，并与其他最先进的矩阵分解模型和深度学习模型进行了比较。