학술논문
Human Mobility Prediction Based on Trend Iteration of Spectral Clustering
Document Type
Periodical
Source
IEEE Transactions on Mobile Computing IEEE Trans. on Mobile Comput. Mobile Computing, IEEE Transactions on. 23(5):4196-4211 May, 2024
Subject
Language
ISSN
1536-1233
1558-0660
2161-9875
1558-0660
2161-9875
Abstract
Human mobility prediction is crucial for epidemic control, urban planning, and traffic forecasting systems. We observe urban traffic flow prediction has a hierarchical structure, in which human mobility prediction should consider not only the spatial and the temporal relationships, but also the high-level mobility trend between individuals and regions. In this paper, we propose a human mobility clustering algorithm based on trend iteration of spectral clustering (TISC) to incorporate the high-level human mobility trend between individuals and regions. We integrate our TISC clustering algorithm with two existing urban traffic flow predictive models: namely, deep spatio-temporal residual network (ST-ResNet) and deep spatio-temporal 3D network (ST-3DNet). By adapting our TISC clustering algorithm, the prediction accuracy of both algorithms has been improved significantly (30.96$\%$% for ST-ResNet and 24.66$\%$% for ST-3DNet). We also compare the TISC-based predictive framework with 26 state-of-the-art human mobility prediction algorithms. We observe that our TISC algorithm considerably outperforms all 26 methods, reducing the predictive error from 6.93% to 69.55$\%$%.