Awesome

Adaptive Spatio-temporal Graph Neural Network for Traffic Forecasting

This is PyTorch implementation of Ada-STNet in the following paper:

Adaptive Spatio-temporal Graph Neural Network for Traffic Forecasting.

Requirements

• scipy
• torch
• tqdm
• h5py
• numpy
• pandas
• PyYAML
• tensorboardX
• torch

Dependency can be installed using the following command:

pip install -r requirements.txt

Data Preparation

Same as DCRNN, the traffic data files for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY), i.e., metr-la.h5 and pems-bay.h5, are available at Google Drive or Baidu Yun, and should be put into the data/ folder. The *.h5 files store the data in panads.DataFrame using the HDF5 file format. Here is an example:

Here is an article about Using HDF5 with Python.

Run the following commands to generate train/test/val dataset at data/{METR-LA,PEMS-BAY}/{train,val,test}.npz.

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

# METR-LA
python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5

Graph Construction

As the currently implementation is based on pre-calculated road network distances between sensors, it currently only supports sensor ids in Los Angeles (see data/sensor_graph/sensor_info_201206.csv).

python -m scripts.gen_adj_mx  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1 --output_pkl_filename=data/sensor_graph/adj_mx.pkl

Besides, the locations of sensors in Los Angeles, i.e., METR-LA, are available at data/sensor_graph/graph_sensor_locations.csv.

Model Training

We utilize the A two-stage training strategy to further enhance the model performance. Here are commands for training the model on METR-LA.

#first stage
python train_stage1.py --config metr-la-stage1-config --name stage-1
#seconde stage
python train_stage2.py --config metr-la-stage2-config --name stage-2

Here are commands for training the model on PEMS-BAY.

#first stage
python train_stage1.py --config pems-bay-stage1-config --name stage-1
#seconde stage
python train_stage2.py --config pems-bay-stage2-config --name stage-2