Graph Neural Controlled Differential Equations for Traffic Forecasting


  • Jeongwhan Choi Yonsei University
  • Hwangyong Choi Yonsei University
  • Jeehyun Hwang Yonsei University
  • Noseong Park Yonsei University



Machine Learning (ML)


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.




How to Cite

Choi, J., Choi, H., Hwang, J., & Park, N. (2022). Graph Neural Controlled Differential Equations for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6367-6374.



AAAI Technical Track on Machine Learning I