Awesome
EALGAP
<p align="center"> <img src="https://github.com/HuiqunHuang/EALGAP/blob/main/Figs/Story.png" width="900" title="Research motivations and potential applications of EALGAP."> </p>Introduction
This repo is the official codes for the paper "Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning", [<a href="https://ieeexplore.ieee.org/document/10184645">paper</a>].
Environment and Dependencies
- Python 3.6
- Tensorflow-GPU-2.3.0
- Keras 2.7.0
- Pandas 1.1.5
- Scikit-learn 0.23.1
- CUDA 10.1
- CuDNN 7.6
Model Training & Evaluation
python MainPredictionFunction/NYC_EALGAP_Main.py
Citations
If you were using our codes or found this repository useful, please consider citing our work:
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{huang2023extreme, title={Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning}, author={Huang, Huiqun and He, Suining and Tabatabaie, Mahan}, booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)}, pages={1059--1070}, year={2023}, organization={IEEE} }"><pre class="notranslate"><code>@inproceedings{huang2023extreme, title={Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning}, author={Huang, Huiqun and He, Suining and Tabatabaie, Mahan}, booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)}, pages={1059--1070}, year={2023}, organization={IEEE} } </code></pre></div>