Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
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Abstract (arXiv)
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.
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25 of 97 references.
References · 25
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- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
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- 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
- 2019STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecastingcited by 59
13 more
- 2020Coupled Layer-wise Graph Convolution for Transportation Demand Predictioncited by 54
- 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
- 2017Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approachcited by 35
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- 2021Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Predictioncited by 29
- 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
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- 2020Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Networkcited by 14
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- 2015A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networkscited by 2
- 2021An Intelligent Multi-Speed Advisory System using Improved Whale Optimisation Algorithmcited by 2
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