Graph Neural Controlled Differential Equations for Traffic Forecasting

By Jeongwhan Choi, Hwangyong Choi, JeeHyun Hwang, Noseong Park. AAAI Conference on Artificial Intelligence, 2021

477 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8d6a348c4b00da0c30f9f5c77619ea40e441652b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Junbo Zhang
2. Yu Zheng
3. Ming Jin
4. Zezhi Shao
5. Jiawei Jiang
6. Geoffrey I. Webb
7. Nizar Bouguila
8. Cesare Alippi

In Building and Construction

1. Junbo Zhang
2. Yu Zheng
3. Nizar Bouguila
4. Saeed Rahmani
5. Asiye Baghbani
6. Zachary Patterson
7. Yuxuan Liang
8. Zezhi Shao

In Electrical and Electronic Engineering

1. Cesare Alippi
2. Yu Zheng
3. Wayne Xin Zhao
4. Qingsong Wen
5. Junbo Zhang

Citations in the corpus

• PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, background): “2018; Yu, Yin, and Zhu 2018), and thus GNN-based methods have been widely explored in traffic prediction (Wu et al. 2019; Song et al. 2020; Wu et al. 2020; Li and Zhu 2021; Fang et al. 2021; Choi et al. 2022).”
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “STG-NCDE [75] shares a similar idea but operates under the framework of neural controlled differential equations (NCDEs) [129].”
• Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, methodology): “STG-NCDE [220] developed a STGNN combined with the neural controlled differential equation (Neural-CDE) for better continuous modeling, compared with Neural-ODE-based methods.”
• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “dataset used in [221], [222], and [220] contains the data from July to August 2016 obtained from 170 sensors on eight roads.”