Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic accidents, optimizing urban planning, etc. However, due to the complexity of the traffic network, traditional machine learning and statistical methods are relegated to the background. With the advent of the artificial intelligence era, many deep learning frameworks have made remarkable progress in various fields and are now considered effective methods in many areas. As a deep learning method, Graph Neural Networks (GNNs) have emerged as a highly competitive method in the ITS field since 2019 due to their strong ability to model graph-related problems. As a result, more and more scholars pay attention to the applications of GNNs in transportation domains, which have shown excellent performance. However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention. This paper aims to review the applications of GNNs in six representative and emerging ITS domains: traffic forecasting, autonomous vehicles, traffic signal control, transportation safety, demand prediction, and parking management. We have reviewed extensive graph-related studies from 2018 to 2023, summarized their methods, features, and contributions, and presented them in informative tables or lists. Finally, we have identified the challenges of applying GNNs to ITS and suggested potential future directions.

翻译：智能交通系统在缓解交通拥堵、减少交通事故、优化城市规划等方面具有至关重要的作用。然而，由于交通网络的复杂性，传统机器学习与统计方法已逐渐退居次要地位。随着人工智能时代的到来，众多深度学习框架在各个领域取得了显著进展，目前已被视为诸多领域的有效方法。作为深度学习方法之一，图神经网络自2019年起因其在建模图相关问题上的强大能力，在智能交通系统领域展现出极强的竞争力。正因如此，越来越多的学者开始关注图神经网络在交通领域的应用，这些应用已表现出优异性能。然而，该领域大部分研究仍集中于交通预测，而其他智能交通系统领域（如自动驾驶和城市规划）仍需更多关注。本文旨在综述图神经网络在六个具有代表性且新兴的智能交通系统领域中的应用：交通预测、自动驾驶、交通信号控制、交通安全、需求预测和停车管理。我们系统梳理了2018年至2023年间大量与图相关的研究，总结了其方法、特点和贡献，并以信息化的表格或列表形式呈现。最后，我们指出了图神经网络应用于智能交通系统所面临的挑战，并提出了未来潜在的研究方向。