Multi-scale Fusion Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting

This is a PyTorch implementation of Multi-scale Fusion Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting, Junbi Xiao, Wenjing Zhang, Wenchao Weng, Yuhao Zhou*, Yunhuan Cong.

Table of Contents

• configs: training Configs and model configs for each dataset

• lib: contains self-defined modules for our work, such as data loading, data pre-process, normalization, and evaluate metrics.

• model: implementation of our model

Data Preparation

The dataset can be downloaded from STSGCN (AAAI-20).

Unzip the downloaded dataset files to the main file directory, the same directory as run.py.

Requirements

Python 3.6.5, Pytorch 1.9.0, Numpy 1.16.3, argparse and configparser

Model Training

python run.py --datasets {DATASET_NAME} --mode {MODE_NAME}

Replace {DATASET_NAME} with one of PEMSD3, PEMSD4, PEMSD8

such as python run.py --datasets PEMSD4

There are two options for {MODE_NAME} : train and test

Selecting train will retrain the model and save the trained model parameters and records in the experiment folder.

With test selected, run.py will import the trained model parameters from {DATASET_NAME}.pth in the 'pre-trained' folder.