Earticle

초록

영어

In recent years, the anticipation of human mobility flow has significant applications in various domains ranging from urban planning to public health. This study proposes a hybrid Graph Neural Network and Long Short Term- Memory network-based model for nationwide human mobility prediction, effectively capturing inter-urban movement patterns. We validate the feasibility and effectiveness of our model using the Korean internal-city mobility dataset, which captures real-world population movement patterns across various urban regions. Our experimental results accurately predict inter-city mobility, advancing urban planning, health, and transport.

목차

Abstract
1. Introduction
2. Methodology
3. Experiment result
4. Conclusions
Acknowledgment
References

키워드

저자

• Safi Ullah, Amjid Ali [ Digital Contents Research Institute Sejong University ]
• Min Je Kim [ Digital Contents Research Institute Sejong University ]
• Seung Woo Lee [ Sejong University ]
• Young Hwan Lee3 [ Korea University ]
• Sung Wook Baik [ Digital Contents Research Institute Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004