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

TL;DR

This paper reviews recent graph-based machine learning methods and their applications in intelligent transportation systems, highlighting technical challenges, methodologies, and case studies demonstrating their potential to improve urban transportation.

Contribution

It provides an in-depth review of graph-based ML techniques and presents new case studies applying these methods to ITS challenges.

Findings

Graph neural networks effectively model transportation networks.

Case studies show improved efficiency and safety in ITS.

Graph-based approaches offer scalable solutions for urban transportation.

Abstract

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.