Graph Neural Controlled Differential Equations for Traffic Forecasting

News

• June 2024: 📈 Our paper has been cited over 200 times.
• May 2024: 🔝 As of AAAI 2022, our paper, "Graph Neural Controlled Differential Equations for Traffic Forecasting", was ranked 14th among the most influential papers.

Introduction

This is the repository of our accepted AAAI 2022 paper "Graph Neural Controlled Differential Equations for Traffic Forecasting". Paper is available on arxiv.

Citation

If you find this code useful, you may cite us as:

@inproceedings{choi2022STGNCDE,
  title={Graph Neural Controlled Differential Equations for Traffic Forecasting},
  author={Jeongwhan Choi AND Hwangyong Choi AND Jeehyun Hwang AND Noseong Park},
  booktitle={AAAI},
  year={2022}
}

Setup Python environment for STG-NCDE

Install python environment

$ conda env create -f environment.yml 

Reproducibility

Usage

In terminal

• Run the shell file (at the root of the project)

$ bash run.sh

• Run the python file (at the model folder)

$ cd model

$ python Run_cde.py --dataset='PEMSD4' --model='GCDE' --model_type='type1' --embed_dim=10 --hid_dim=64 --hid_hid_dim=64 --num_layers=2 --lr_init=0.001 --weight_decay=1e-3 --epochs=200 --tensorboard --comment="" --device=0

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