As one of the most important applications of industrial Internet of Things, intelligent transportation system aims to improve the efficiency and safety of transportation networks. In this article, we propose a novel Bayesian framework entitled variational graph recurrent attention neural networks (VGRAN) for robust traffic forecasting. It captures time-varying road-sensor readings through dynamic graph convolution operations and is capable of learning latent variables regarding the sensor representation and traffic sequences. The proposed probabilistic method is a more flexible generative model considering the stochasticity of sensor attributes and temporal traffic correlations. Moreover, it enables efficient variational inference and faithful modeling of implicit posteriors of traffic data, which are usually irregular, spatial correlated, and multiple temporal dependents. Extensive experiments conducted on two real-world traffic datasets demonstrate that the proposed VGRAN model outperforms state-of-the-art approaches while capturing innate ambiguity of the predicted results.

中文翻译：

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变图神经网络在智能交通系统中道路交通预测中的应用

智能交通系统是工业物联网最重要的应用之一，旨在提高交通网络的效率和安全性。在本文中，我们提出了一种新颖的贝叶斯框架，该框架名为“变分图递归注意神经网络（VGRAN）”，用于可靠的流量预测。它通过动态图形卷积操作捕获时变的道路传感器读数，并能够学习有关传感器表示和交通序列的潜在变量。考虑到传感器属性和时间流量相关性的随机性，所提出的概率方法是一种更灵活的生成模型。此外，它还可以对交通数据的隐式后代进行有效的变分推断和忠实建模，这些数据通常是不规则的，空间相关的，和多个时间依存者。在两个真实世界的交通数据集上进行的广泛实验表明，所提出的VGRAN模型在捕获预测结果的固有歧义的同时，胜过了最新方法。