Graph Neural Network for Traffic Forecasting: A Survey

TL;DR

This survey reviews the application of graph neural networks in traffic forecasting, highlighting recent advancements, open resources, and future research directions in modeling spatial and temporal dependencies.

Contribution

It is the first comprehensive survey exploring how graph neural networks are used in various traffic forecasting problems, including data resources and future directions.

Findings

Graph neural networks achieve state-of-the-art performance in traffic forecasting.

A comprehensive list of open data and resources is provided.

Future research directions are identified and discussed.

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 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.