HuiqunHuang / EALGAP

Official codes for the paper "Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning"

EALGAP

Introduction

This repo is the official codes for the paper "Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning", [paper].

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:

@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}
}