Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems (ITS). However, the collected traffic state data are often incomplete in the real world. In this paper, a novel deep learning framework is proposed to use information from adjacent links to estimate road traffic states. First, the representation of the road network is realized based on graph embedding (GE). Second, with this representation information, the generative adversarial network (GAN) is applied to generate the road traffic state information in real-time. Finally, two typical road networks in Caltrans District 7 and Seattle area are adopted as cases study. Experimental results indicate that the estimated road traffic state data of the detectors have higher accuracy than the data estimated by other models.

交通状态估计是智能交通系统（ITS）中至关重要的基本功能。但是，在现实世界中，收集的交通状态数据通常是不完整的。在本文中，提出了一种新颖的深度学习框架，以使用来自相邻链接的信息来估计道路交通状况。首先，基于图嵌入（GE）实现道路网络的表示。其次，利用该表示信息，应用生成对抗网络（GAN）实时生成道路交通状态信息。最后，以Caltrans 7区和西雅图地区的两个典型公路网为案例研究。实验结果表明，检测器的道路交通状态估计数据比其他模型估计的数据具有更高的准确性。