PINN-GNN Traffic Flow Prediction

This repository contains the implementation of PINN-GNN (Physics-Informed Graph Neural Networks), an innovative approach to traffic forecasting under flooding that combines the power of Graph Neural Networks with the principles of Physics-Informed Neural Networks.

Our proposed model has been rigorously tested on various traffic datasets, demonstrating superior performance in: 1) Accuracy of traffic flow predictions 2) Robustness to missing or noisy data 3) Generalization to unseen traffic scenarios

About the Model

PINN-GNN addresses the complex task of traffic forecasting by integrating physical laws and constraints into the learning process of graph neural networks. Our model uniquely features:

1. Graph Neural Networks (GNN): To capture the spatial dependencies in the traffic network.

2. Physics-Informed Neural Networks (PINN): To incorporate traffic flow dynamics and physical constraints.

3. Field Effect Module: To model long-range dependencies and global traffic patterns.

Full text (PDF) is available at updating.

Dependencies

OS

Linux systems (e.g. Ubuntu and CentOS).

Python

The code is built based on Python 3.9, PyTorch 1.13.0, and EasyTorch. You can install PyTorch following the instruction in PyTorch.

Miniconda or Anaconda are recommended to create a virtual python environment.

We implement our code based on BasicTS.

Other Dependencies

pip install -r requirements.txt

Getting started

Download Data

You can download data from google drive and unzip it.

Preparing Data

• Pre-process Data The further description about data pre-processing can be found in data_preparation. After that, you can pre-process all datasets by

  cd /path/to/your/project bash scripts/data_preparation/all.sh

Then the dataset directory will look like this:

datasets
   ├─PEMS04
   ├─PEMS08
   ├─ cities
   |    ├─ augsburg
   |    ├─ cagliari
   |    ├─ darmstadt
   |    ├─ essen
   |    ├─ hamburg
   |    ├─ innsbruck
   |    ├─ london
   |    ├─ luzern
   |    ├─ madrid
   |    ├─ manchester
   |    ├─ marseille
   |    ├─ paris
   |    ├─ strasbourg
   |    ├─ taipeh
   |    └─ toronto
   ├─README.md

Pre-Training on Models

cd /path/yourproject

Then run the folloing command to run in Linux screen.

screen -d -m python pinngnn/run.py --cfg='pinngnn/PEMS04.py' --gpus='0'

screen -d -m python pinngnn/run.py --cfg='pinngnn/PEMS08.py' --gpus='0'

Downstream Predictor

After pre-training , copy your pre-trained best checkpoint to mask_save/. For example:

cp checkpoints/pinngnn/064b0e96c042028c0ec44856f9511e4c/pinngnn_best_val_MAE.pt mask_save/pinngnn_PEMS04_864.pt
cp checkpoints/pinngnn/50cd1e77146b15f9071b638c04568779/pinngnn_best_val_MAE.pt mask_save/pinngnn_PEMS08_864.pt

Then run the predictor as :

screen -d -m python pinngnn/run.py --cfg='pinngnn/PEMS04.py' --gpus='0' 

screen -d -m python pinngnn/run.py --cfg='pinngnn/PEMS08.py' --gpus='0'

• To find the best result in logs, you can search best_ in the log files.

Citation

If you use this code in your project, please consider citing the following paper:

@article{ updating
}

License

Please see the license for further details.