Urban flow generation

The official PyTorch implementation of "Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion" (SIGSPATIAL'23). (PDF on arxiv)

NYC, Washington D.C. (DC), and Baltimore (BM) datasets are included.

The code is tested under a Linux desktop with torch 1.9.0 and Python 3.8.10.

Installation

Environment

• Tested OS: Linux
• Python >= 3.8
• PyTorch == 1.9.0
• torch_geometric == 1.7.2

Dependencies

1. Install PyTorch 1.9.0 with the correct CUDA version.
2. Use the pip install -r requirements.txt command to install all of the Python modules and packages used in this project.

Usage

Step-1 Pretrain to get KG embeddings:

bash pretrain.sh

Step-2 Train diffusion model:

bash train.sh

Step-3 Generate urban flow:

bash sample.sh

Step-4 Evaluate generated flow:

python evaluate.py

(The default dataset is NYC, you can modify the dataset in pretrain.sh, train.sh, sample.sh, and evaluate.py)

More Related Works

• Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network (WWW '23)

Note

The implemention is based on DDPM.

If you found this library useful in your research, please consider citing:

@inproceedings{zhou2023towards,
  title={Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion},
  author={Zhou, Zhilun and Ding, Jingtao and Liu, Yu and Jin, Depeng and Li, Yong},
  booktitle={Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
  pages={1--12},
  year={2023}
}