README.md

GCN-M: Graph Convolutional Networks for Traffic Forecasting with missing values

This is the companion repository for our paper titled Graph Convolutional Networks for Traffic Forecasting with missing values published in Data Mining and Knowledge Discovery and also available on ArXiv.

Requirements:

• matplotlib == 3.2.1
• numpy == 1.19.2
• pandas == 0.25.1
• scikit_learn == 0.21.2
• torch == 1.6.0
• tensorwatch == 0.9.1

Dependencies can be installed using the following command:

pip install -r requirements.txt

Data

Step1:

• Download METR-LA and PEMS-BAY data from Google Drive or Baidu Yun links provided by DCRNN.
• Put the downloaded data into the repository mentioned in "config/DATASET.conf"

Step2: Preprocess raw data

python data/generate_dated_data.py

Usage

python main.py --config CONFIG_FILE --itr NBR_ITERATION

Citation

If you find this repository useful in your research, please consider citing the following paper:

@article{zuo2023graph,
  title     = {Graph convolutional networks for traffic forecasting with missing values},
  author    = {Zuo, Jingwei and Zeitouni, Karine and Taher, Yehia and Garcia-Rodriguez, Sandra},
  journal   = {Data Mining and Knowledge Discovery},
  volume    = {37},
  number    = {2},
  pages     = {913--947},
  year      = {2023},
  publisher = {Springer}
}