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SpatialGAT: Spatial Attention Based Grid Representation Learning for Predicting Origin-Destination Flow

Python Version

Overview

This is the implementation of the spatialGAT in the paper: Spatial attention based grid representation learning for predicting origin–destination flow in IEEE Big Data 2022. We collected multimodal characteristics of regions, such as road network densities and facility distributions, from several open-source datasets and used them as grid signals, and constructed a spatial attention-based deep graph network to generate grid embeddings and used them to predict the OD volumes.

Dependencies

  • numpy
  • pandas
  • torch
  • torch_geometric
  • matplotlib
  • scikit-learn
  • jismesh
  • pyproj

Prepare environment

conda create -n spatialGAT
conda activate spatialGAT
conda install pip
python -m pip install -r requirement.txt

After downloading this repository, run:

cd spatialGAT
mkdir result
mkdir explain

About Data

  • OD data

The OD data used in the paper come from SoftBank Group Corporation, and we are not allowed to open them to the public. Currently, we provide a sample file to indicate the format of OD volumes. Now we are preparing synthetic data in the same scope using PseudoPFlow data. Once it is finished, we will update them in this repository.

  • Region attributes

You can find them in the data folder. You can also download the files from URLs provided in the paper.

To Run Codes

How to generate the prediction

Run the following code:

python main.py

How to generate the explanation

First put the generated model.pth file into the explain folder. Then run the explanation generation module:

python explain.py

You can find the diagram of feature important analysis in the explain folder.

Citation

@inproceedings{cai2022spatial,
  title={Spatial attention based grid representation learning for predicting origin--destination flow},
  author={Cai, Mingfei and Pang, Yanbo and Sekimoto, Yoshihide},
  booktitle={2022 IEEE International Conference on Big Data (Big Data)},
  pages={485--494},
  year={2022},
  organization={IEEE}
}