https://github.com/xovee/stcf
STCF: Spatial-Temporal Contrasting for Fine-Grained Urban Flow inference. IEEE Transactions on Big Data, 2023.
https://github.com/xovee/stcf
contrastive-learning fine-grained smart-city urban-computing urban-flow-inference
Last synced: about 1 year ago
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STCF: Spatial-Temporal Contrasting for Fine-Grained Urban Flow inference. IEEE Transactions on Big Data, 2023.
- Host: GitHub
- URL: https://github.com/xovee/stcf
- Owner: Xovee
- License: mit
- Created: 2021-09-24T12:27:01.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-12T06:47:27.000Z (over 2 years ago)
- Last Synced: 2025-03-20T23:42:08.758Z (about 1 year ago)
- Topics: contrastive-learning, fine-grained, smart-city, urban-computing, urban-flow-inference
- Language: Python
- Homepage: https://www.xoveexu.com
- Size: 16.8 MB
- Stars: 12
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# STCF: Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
**Left**: coarse-grained taxi flow map;
**Right**: inferred fine-grained taxi flow map by STCF.
This repo provides a reference implementation of **STCF** framework described in the following paper:
> **Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference**
> Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, and Goce Trajcevski
> IEEE Transactions on Big Data, vol. 9, no. 6, pp. 1711--1125, Nov 2023.
> https://doi.org/10.1109/TBDATA.2023.3316471
## Data
We use [TaxiBJ](https://github.com/yoshall/UrbanFM) P1-P4 and [BikeNYC](https://www.ijcai.org/proceedings/2020/180) datasets.
You can download all five datasets at:
- [Google Drive](https://drive.google.com/drive/folders/1_YgQfrNVrJzsyoTPvu1uhV40tnpuBYVK?usp=sharing)
- [Baidu Drive](https://pan.baidu.com/s/1r4G4xYtAdamcBaO3V-S01w) (password: `ndep`)
## Environment
[Update] Latest Tensorflow environment supported.
In this repo, STCF is implemented by `Python 3.9`, `TensorFlow 2.9.1`, `cudatoolkit 11.2`, and `cudnn 8.1.0`.
Note: above environment requires NVIDIA driver version `>450.80.02`.
Create a virtual environment and install GPU-support packages via [Anaconda](https://www.anaconda.com/):
```shell
# create virtual environment
conda create --name=stcf python=3.9
conda activate stcf
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
# install tensorflow==2.9.1, scikit-learn>=1.0
pip install -r requirements.txt
```
If `tensorflow` cannot identify `cudatoolkit`, try to configure the system path as shown in the `4. GPU setup` section of this [guide](https://www.tensorflow.org/install/pip).
## Usage
**Step 1**: Pre-train the spatial- and temporal-contrasting networks:
```shell
# spatial
python code/sc_pretrain.py --dataset taxi-bj/p1 --model sc
# temporal
python code/tc_pretrain.py --dataset taxi-bj/p1 --model tc
```
**Step 2**: Fine-tune the coupled network and evaluate performance:
```shell
# fine-tune
python code/stcf.py --dataset taxi-bj/p1 --sc sc --tc tc --model stcf
# evaluate
python code/evaluate.py --dataset taxi-bj/p1 --model stcf
```
More options are described in the code.
## Cite
If you find our paper & code are helpful for your research,
please consider citing us :heart_decoration:
```bibtex
@article{xu2023stcf,
author = {Xovee Xu and Zhiyuan Wang and Qiang Gao and Ting Zhong and Bei Hui and Fan Zhou and Goce Trajcevski},
title = {Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference},
journal = {IEEE Transactions on Big Data},
year = {2023},
volume = {9},
number = {6},
pages = {1711--1725},
doi = {10.1109/TBDATA.2023.3316471},
}
```
## Acknowledgment
We are particularly grateful for the assistance given by Yuhao Liang and Ce Li.
We would like to show our gratitude to the authors of UrbanFM,
FODE, UrbanODE, and others, for sharing their data and codes.
We express our gratitude to reviewers and editors for giving constructive feedbacks.
This work was initially submitted to a conference in January, 2021.
## Contact
For any questions,
please open an issue or drop an email to: `xovee.xu at gmail.com`