Fantasy-Shaw / Bi-STAT-Remaster

Modified code of the paper Bi-STAT for educational use.

Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Forecasting

Requirements

Python 3.7.3
Pytorch 1.9.0
Numpy 1.19.5
argparse

Dataset

The datasets (PEMSD3, PEMSD4, PEMSD7 and PEMSD8) used in our experiments are available at STSGCN.

Project Structure

• lib: the codes to to construct the graph matrix and the spatial embedding matrix, and the common utils such as data loading, pre-processing and normalization, evaluation.

• models: implementation of our Bi-STAT model

Run

• (1) Get the sensor graph for the dataset

  python construct_adj.py

• (2) Generate the spatial embedding for the dataset

  python generate_SE.py

• (3) Run our Bi-STAT model

  python run.py