Awesome

#Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting

This is a Pytorch implementation of Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting, which combines the graph attention convolution (GAT) and the dilated convolution structure with gate mechanisms.

Requirements

• see requirements.txt

Data Preparation

1. Download METR-LA and PEMS-BAY data from Google Drive or Baidu Yun links provided by DCRNN. metr-la.h5 and pems-bay.h5should be put into the data/ folder.

# Create data directoriesdfrt yui 
mkdir -p data/{METR-LA,PEMS-BAY}

# METR-LA
python data_preparation.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python data_preparation.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5

Run Demo

# change 'base_path' and 'best_model_path' to choose which dataset you want
python run_demo.py --base_path=./pre_train_model/BAY_dataset --best_model_path=stgat_1.45.pkl

# run pre trained models (stgcn, gwnet models about BAY datasets will be added)
python run_demo_baselines.py

Folder structure

├── baselines
│   ├── experiment_base
│   ├── __init__.py
│   ├── gwnet.py
│   ├── rnn.py
│   ├── run_demo_baselines.py
│   ├── stgcn.py
│   └── train_base.csv
├── data
│   ├── METR-LA
│   ├── PEMS-BAY
│   ├── sensor_graph
│   ├── metr-la.h5
│   └── pems-bay.h5
├── experiment
├── pre_train_model
│   ├── BAY_dataset
│   └── LA_dataset
│
├── data_preparation.py
├── draw.py
├── model_stgat.py
├── README.md
├── requirements.txt
├── run_demo.py
├── train.py
└── util.py