Earticle

초록

영어

Management of Population movement among cities is important for many sectors, such as urban planning, emergency management, and traffic management, etc. The early approaches, including radiation and gravity, give the primary insights but struggle to capture the complex topological and directional nature of inter-city mobility networks. This paper presents a Graph Convolutional Network (GCN)-based framework for modeling and predicting population movement between Chinese cities using Baidu mobility data. Cities are represented as nodes in a directed graph, with weighted edges indicating monthly outbound flows. A multi-layer GCN learns node embeddings that encode both local and global spatial dependencies, enabling the prediction of continuous relationship scores that reflect the intensity of movement. Experimental results, MedAPE of 5.59 and MAPE of 19.36, as well as relationship scores from major cities such as Shanghai and Shijiazhuang, demonstrate that the model effectively identifies stable mobility corridors and evolving connections over time. Overall, the proposed approach provides interpretable insights into population mobility dynamics and supports data-driven decision-making in urban forecasting and regional policy design.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Data Preparation
B. Graph Convolutional Network (GCN)
C. Proposed Method
III. RESULTS AND DISCUSSION
A. Experimental Setup
B. Dataset Descriptions
C. State-Of-The-Art (SOTA) Comparison
D. Discussion
E. Visual Results
IV. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

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

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