Earticle

초록

영어

Predicting accurate human migration patterns is crucial for effective urban planning. However, accurate human migration patterns prediction remains a challenging task. Existing methods, such as Graph Neural Network approaches, often overlook dynamic temporal variations and directional dependencies in large-scale migration data. To overcome this challenge, we propose MiGA-Net (Migration Graph Attention Network), a graph Neural network-based framework enhanced with an attention mechanism to capture complex spatiotemporal dependencies and highlight significant migration flows on the domestic and international level. We utilize two different datasets of the Shinan-gun, South Korea, for international and domestic regions. Experimental results show that the proposed MiGA-Net achieved superior performance over both datasets. The model achieved 0.0027 MAE for domestic flow and 0.0155 for international flow, demonstrating the effectiveness of the proposed framework.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Data Preprocessing.
B. Graph Neural Network.
C. Additive Attention Mechanism
III. RESULTS AND DISCUSSION
A. Datasets.
B. Evaluation Metrics and Results
C. Domestic Migration Results
D. International Migration Results
E. Comparison with Spatio-Temporal Models
IV. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Safi Ullah [ Sejong University Seoul 143-747, South Korea ]
• Adnan Hussain [ Sejong University Seoul 143-747, South Korea ]
• Kaleem Ullah [ Sejong University Seoul 143-747, South Korea ]
• Muhammad Munsif [ Sejong University Seoul 143-747, South Korea ]
• Amjid Ali [ Sejong University Seoul 143-747, South Korea ]
• Aizaz Ali Shah [ Sejong University Seoul 143-747, South Korea ]
• Muhammad Afaq [ Sejong University Seoul 143-747, South Korea ]
• Sung Wook Baik [ Sejong University Seoul 143-747, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004