Efficient machine learning algorithms capturing both spatial and temporal dependencies offer promising results for many fields including traffic prediction. However, their performance is still limited, particularly in congested areas where the speed on a road varies significantly. This paper exploits spatial (congestion) dependencies in road networks to improve the performance of spatio-temporal deep learning algorithms. Essentially, we extract spatial information relevant to how congestion develops and evolves in the network and enhance deep learning algorithms with it by developing custom prediction models for various network components. This research identifies traffic patterns through graph theory and traffic flow fundamentals, integrates them with deep learning algorithms for traffic prediction purposes, and enables better predictions in critically congested areas of road networks. The case studies with New York and Amsterdam networks show promising results; the proposed enhanced models significantly outperform the original deep learning models that consider the whole network as the prediction domain.

捕获空间和时间依赖性的高效机器学习算法为包括交通预测在内的许多领域提供了令人鼓舞的结果。但是，它们的性能仍然很有限，特别是在拥挤的地区，那里的行车速度变化很大。本文利用道路网络中的空间（拥塞）依赖性来提高时空深度学习算法的性能。本质上，我们提取与网络拥塞如何发展和演变有关的空间信息，并通过为各种网络组件开发自定义预测模型来增强深度学习算法。这项研究通过图论和交通流基础知识来识别交通模式，并将其与深度学习算法集成在一起，以进行交通预测，并能在道路网严重拥挤的地区进行更好的预测。纽约和阿姆斯特丹网络的案例研究显示出令人鼓舞的结果；提出的增强模型明显优于将整个网络视为预测域的原始深度学习模型。