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The Intelligent Transportation System (ITS) is an important part of moderntransportation infrastructure, employing a combination of communicationtechnology, information processing and control systems to manage transportationnetworks. This integration of various components such as roads, vehicles, andcommunication systems, is expected to improve efficiency and safety byproviding better information, services, and coordination of transportationmodes. In recent years, graph-based machine learning has become an increasinglyimportant research focus in the field of ITS aiming at the development ofcomplex, data-driven solutions to address various ITS-related challenges. Thischapter presents background information on the key technical challenges for ITSdesign, along with a review of research methods ranging from classicstatistical approaches to modern machine learning and deep learning-basedapproaches. Specifically, we provide an in-depth review of graph-based machinelearning methods, including basic concepts of graphs, graph datarepresentation, graph neural network architectures and their relation to ITSapplications. Additionally, two case studies of graph-based ITS applicationsproposed in our recent work are presented in detail to demonstrate thepotential of graph-based machine learning in the ITS domain.

Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems