Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction

Related works

1. TCN: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
2. GLU and GTU: Language Modeling with Gated Convolutional Networks
3. ChebNet: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
4. GCN: Semi-Supervised Classification with Graph Convolutional Networks

Related code

1. TCN: https://github.com/locuslab/TCN
2. ChebNet: https://github.com/mdeff/cnn_graph
3. GCN: https://github.com/tkipf/pygcn

Dataset

Source

1. METR-LA: DCRNN author's Google Drive
2. PEMS-BAY: DCRNN author's Google Drive
3. PeMSD7(M): STGCN author's GitHub repository

Preprocessing

Using the formula from ChebNet：

Differents of code between mine and author's

1. Fix bugs
2. Add Early Stopping approach
3. Add Dropout approach
4. Offer a different set of hyperparameters
5. Offer config files for two different categories graph convolution (ChebyGraphConv and GraphConv)
6. Add datasets METR-LA and PEMS-BAY
7. Adopt a different data preprocessing method

Requirements

To install requirements:

pip3 install -r requirements.txt