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Smart city article details
| Title | Human Mobility Prediction Based On Trend Iteration Of Spectral Clustering |
|---|---|
| ID_Doc | 29611 |
| Authors | Jia W.; Zhao S.; Zhao K. |
| Year | 2024 |
| Published | IEEE Transactions on Mobile Computing, 23, 5 |
| DOI | http://dx.doi.org/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 humanmobility 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 TISCbased 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%. © 2023 IEEE. |
| Author Keywords | deep learning; Human mobility prediction; smart city; spectral clustering |
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