PDG2Seq: Periodic Dynamic Graph to Sequence model for Traffic Flow Prediction

This is the code repository for the Periodic Dynamic Graph to Sequence model (PDG2Seq).

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

• pre-trained: pre-trained model parameters

Usage Instructions for Hyperparameters

days_per_week: The time intervals for data collection vary across different datasets. Adjust this hyperparameter based on the time intervals of the dataset being used. For example, in the PEMS04 dataset with a time interval of 5 minutes, set this parameter to 14400/5=288. Similarly, in the NYC-Bike dataset with a time interval of 30 minutes, set this parameter to 14400/30=48.

steps_per_day: The data collection scope varies across different datasets. For instance, PEMS04 collects data from Monday to Sunday, so set this parameter to 7. Conversely, for the PEMS07(M) dataset, data is collected only from Monday to Friday, so set this parameter to 5.

Data Preparation

For convenience, we package these datasets used in our model in Google Drive.

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 --dataset {DATASET_NAME} --mode {MODE_NAME}

Replace {DATASET_NAME} with one of PEMSD3, PEMSD4, PEMSD7, PEMSD8, PEMSD7(L), PEMSD7(M)

such as python run.py --dataset 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.