Graph Neural Network for Traffic Forecasting: The Research Progress

• Published in: ISPRS international journal of geo-information Vol. 12; no. 3; p. 100
• Main Authors: Jiang, Weiwei, Luo, Jiayun, He, Miao, Gu, Weixi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Artificial intelligence; Computer science; data collection; Datasets; Decision trees; Deep learning; Forecasting; graph attention network; graph convolutional network; graph neural network; Graph neural networks; Intelligent transportation systems; Internet of Things; Literature reviews; Machine learning; Mathematical models; Neural networks; Route planning; Source code; Statistical analysis; Statistical models; Surveys; Time series; traffic; Traffic control; Traffic flow; traffic forecasting; Traffic planning; Transportation networks; Trends; Variables; Vehicle routing
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi12030100

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.