The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.

翻译：智能交通系统（ITS）是现代交通基础设施的重要组成部分，它融合了通信技术、信息处理与控制系统来管理交通网络。这种将道路、车辆和通信系统等各类组件集成的架构，有望通过提供更优的信息、服务和交通模式协调来提升效率与安全性。近年来，基于图的机器学习已成为ITS领域日益重要的研究方向，旨在开发复杂的数据驱动解决方案以应对各类ITS相关挑战。本章首先介绍了ITS设计面临的重大技术挑战背景信息，继而系统评述了从经典统计方法到现代机器学习与深度学习方法的演进历程。我们重点深入探讨了基于图的机器学习方法，涵盖图的基本概念、图数据表示、图神经网络架构及其与ITS应用的关联。此外，通过我们近期工作中提出的两个基于图的ITS应用案例研究，详细展示了基于图的机器学习在ITS领域的应用潜力。