Graph Neural Network for Traffic Forecasting: A Survey
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by
Weiwei Jiang, Jiayun Luo
2021
Abstract
Traffic forecasting is an important factor for the success of intelligent
transportation systems. Deep learning models including convolution neural
networks and recurrent neural networks have been applied in traffic forecasting
problems to model the spatial and temporal dependencies. In recent years, to
model the graph structures in the transportation systems as well as the
contextual information, graph neural networks (GNNs) are introduced as new
tools and have achieved the state-of-the-art performance in a series of traffic
forecasting problems. In this survey, we review the rapidly growing body of
recent research using different GNNs, 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, demand forecasting in ride-hailing platforms, etc. We also present a
collection of open data and source resources for each problem, as well as
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 to update the latest papers, open data and source resources.
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