Efforts devoted to mitigate the effects of road traffic congestion have been conducted since 1970s. Nowadays, there is a need for prominent solutions capable of mining information from messy and multidimensional road traffic data sets with few modeling constraints. In that sense, we propose a unique and versatile model to address different major challenges of traffic forecasting in an unsupervised manner. We formulate the road traffic forecasting problem as a latent variable model, assuming that traffic data is not generated randomly but from a latent space with fewer dimensions containing the underlying characteristics of traffic. We solve the problem by proposing a variational autoencoder (VAE) model to learn how traffic data are generated and inferred, while validating it against three different real-world traffic data sets. Under this framework, we propose an online unsupervised imputation method for unobserved traffic data with missing values. Additionally, taking advantage of the low dimension latent space learned, we compress the traffic data before applying a prediction model obtaining improvements in the forecasting accuracy. Finally, given that the model not only learns useful forecasting features but also meaningful characteristics, we explore the latent space as a tool for model and data selection and traffic anomaly detection from the point of view of traffic modelers.

自1970年代以来，一直致力于减轻道路交通拥堵的影响。如今，需要能够在很少的建模约束的情况下从凌乱和多维的道路交通数据集中挖掘信息的出色解决方案。从这个意义上讲，我们提出了一种独特且通用的模型，以无人监督的方式应对流量预测的不同主要挑战。我们将道路交通预测问题公式化为潜在变量模型，假设交通数据不是随机生成的，而是从具有较少维度的潜在空间（包含交通的基本特征）生成的。我们通过提出一种变分自动编码器（VAE）模型来解决该问题，以学习如何生成和推断交通数据，同时针对三个不同的现实世界交通数据集进行验证。在此框架下，我们针对缺失值的未观测交通数据提出了一种在线无监督插补方法。此外，利用学习到的低维潜在空间，我们在应用预测模型之前压缩流量数据，从而获得了预测准确性的提高。最后，鉴于该模型不仅学习有用的预测功能，而且还具有有意义的特征，因此我们从交通建模者的角度探索作为模型和数据选择以及交通异常检测工具的潜在空间。