Graph Neural Network for Traffic Forecasting: A Survey

By Weiwei Jiang, Jiayun Luo. Expert systems with applications, 2021

1,265 citations recorded by the source corpus.

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/bad3534cc797606d1fe3cb09713407783e77cac4

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Citations in the corpus

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (2022, neutral): “Due to the space limitation, we refer you to the recent surveys (Jiang et al. 2021; Jiang and Luo 2021; Li et al. 2021) on traffic forecasting with deep learning.”
A Survey on Deep Learning for Human Mobility (2020, neutral): “A few surveys discuss DL approaches to spatio-temporal data mining [197], tasks related to the smart city ecosystem [32] or traffic-related issues [86, 200], covering some aspects related to human mobility but without a specific focus on the challenges and solutions to mobility-related tasks.”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, background): “Graphs that make up the basis of GNNs can be categorized as Directed/Undirected, Static/Dynamic, Homo-geneous/Heterogeneous, Hypergraphs, and Signed graphs [8].”
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, methodology): “We further examine some recently emerging approaches that integrated STGNN with other advanced learning frameworks.”
Is Homophily a Necessity for Graph Neural Networks? (2021, background): “, 2020a), traffic modeling(Jiang and Luo, 2021), search and discovery (Ying et al.”