Work

Graph Neural Controlled Differential Equations for Traffic Forecasting

Year: 2022

Type: article

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.

Source: AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))

DOI: 10.1609/aaai.v36i6.20587

Authors: Choi, Jeongwhan, Choi, Hwangyong, Hwang, Jeehyun, Park, Noseong

Institution: Yonsei University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funders: Yonsei University, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Awards: G3692948339, G4007600970, G848056306