Articlegreen2022

Graph Neural Controlled Differential Equations for Traffic Forecasting

Jeongwhan Choi · Hwangyong Choi · Noseong Park

384 citations0/0 files storedAAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))

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Traffic Prediction and Management Techniques Traffic control and management Advanced Graph Neural Networks

Abstract

Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). Neural controlled differential equations (NCDEs) are a breakthrough concept for processing sequential data. We extend the concept and design two NCDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2019Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

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2023Graph Neural Networks for Intelligent Transportation Systems: A Survey 2023A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting

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