| Title |
Human Mobility Prediction Based on Trend Iteration of Spectral Clustering |
| ID_Doc |
44013 |
| Authors |
Jia, WZ; Zhao, SJ; Zhao, K |
| Title |
Human Mobility Prediction Based on Trend Iteration of Spectral Clustering |
| Year |
2024 |
| Published |
Ieee Transactions On Mobile Computing, 23, 5 |
| DOI |
10.1109/TMC.2023.3288132 |
| 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 % . |
| Author Keywords |
Human mobility prediction; spectral clustering; deep learning; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001198016900068 |
| WoS Category |
Computer Science, Information Systems; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
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