Accurate traffic speed prediction is critical to modern internet of things-based intelligent transportation systems. It serves as the foundation of advanced traffic management systems and travel services. Nonetheless, the large number of roads and sensors impose great computational burden to existing forecast approaches, most of which can only handle one or few roads at a time. In this paper, a novel data-driven deep learning-based approach is proposed for citywide traffic speed prediction. The proposed approach is grounded on recent developments of geometric deep learning techniques to fully utilize the topological information of road networks in the learning process. Specifically, the approach captures the geometric traffic data dependency with graph convolution and attention mechanisms, and the temporal data correlation is extracted and expanded using the encoder–decoder architecture within a generative adversarial learning framework. Comprehensive case studies are conducted with real-world urban road networks and respective data to evaluate its performance, where consistent improvements can be observed over baseline approaches. Lastly, an architectural study is carried out to discover the best-performing structure of the proposed approach, whose sensitivity to data noise and sample frequency is also assessed.

准确的交通速度预测对于基于现代物联网的智能交通系统至关重要。它是高级交通管理系统和旅行服务的基础。尽管如此，大量的道路和传感器给现有的预测方法带来了很大的计算负担，其中大多数方法一次只能处理一条或几条道路。在本文中，提出了一种基于数据驱动的深度学习的新方法，用于城市范围内的交通速度预测。所提出的方法基于几何深度学习技术的最新发展，以在学习过程中充分利用道路网络的拓扑信息。具体来说，该方法利用图卷积和注意力机制捕获了几何交通数据的依赖性，在生成的对抗学习框架内，使用编解码器架构提取和扩展时间数据的相关性。结合实际的城市道路网络和相应的数据进行了综合案例研究，以评估其性能，与基线方法相比，可以观察到一致的改进。最后，进行了一项架构研究，以发现所提出方法的最佳性能结构，并评估了其对数据噪声和采样频率的敏感性。