Graph neural network for traffic forecasting: A survey
- Vol. 207, pp 117921-117921
TLDR
In this paper , the authors present a comprehensive survey of graph neural networks for traffic forecasting problems, including graph convolutional and graph attention networks, and a comprehensive list of open data and source codes for each problem.Abstract:
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 codes 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 codes will be updated. read more
Citations
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A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction
Yong Chen,Xiqun Chen +1 more
TL;DR: In this article , a reinforced dynamic graph convolutional network model is proposed to simultaneously conduct data imputation and network-wide traffic flow prediction, which can effectively extract the data missing features and spatio-temporal dependence features between the stations.
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Traffic congestion propagation inference using dynamic Bayesian graph convolution network
TL;DR: Wang et al. as discussed by the authors proposed a dynamic Bayesian graph convolutional network (DBGCN), which integrates Bayesian inference into a deep learning framework to infer the congestion propagation spatiotemporal coverage and reveal variations in congestion propagation patterns according to the road network structure.
Journal ArticleDOI
Graph Neural Networks in IoT: A Survey
Guimin Dong,Mingyue Tang,Zhiyuan Wang,Jiechao Gao,Sikun Guo,Lihua Cai,Robert Gutierrez,Brad Campbell,Laura E. Barnes,Mehdi Boukhechba +9 more
TL;DR: A comprehensive review of recent advances in the application of graph neural networks to the IoT field is presented, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions.
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Similarity-navigated graph neural networks for node classification
TL;DR: Wang et al. as mentioned in this paper proposed a Similarity-Navigated Graph Neural Network (SNGNN) which uses Node Similarity matrix coupled with mean aggregation operation instead of the normalized adjacency matrix in the neighborhood aggregation process.
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Bike sharing usage prediction with deep learning: a survey
TL;DR: In this article , a survey of the latest studies about bike sharing usage prediction with deep learning is reviewed, with a classification for the prediction problems and models, both within and beyond bike sharing systems.
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