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Spatial–temporal grid clustering method based on frequent stay point recognition

  • S.I.: AI-based Web Information Processing
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Abstract

In order to identify geolocation of defaulter and extract travel information from trajectory data, spatial–temporal grid clustering method are adopted to analysis massive trajectory data. Firstly, the trajectory data are preprocessed, and the spacetime cluster method is applied to detect the travelers’ geolocation information based on the information the travel segments are extracted. Secondly, for the recognition of frequent stay point, we proposed the spatial–temporal grid clustering model with smooth trajectory division algorithm and which improve the efficiency of processing a large amount of trajectory data. Thirdly, we proposed the spatial–temporal grid clustering method based on frequent stay point recognition. The experiment results of stationary trajectory division indicate that the frequent stay point and frequent paths can be effectively excavated under the condition of small information loss. These results demonstrate convincingly the effectiveness of the proposed method.

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Acknowledgements

The authors acknowledge the Supported by National Key Research and Development Program of China (2018YFC0830400).

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Authors and Affiliations

  1. Blockchain Laboratory, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

    Bin Zhang, Qiuxia Wang, Jing Li & Zhou Ye

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  1. Bin Zhang
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  2. Qiuxia Wang
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  3. Jing Li
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  4. Zhou Ye
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Correspondence to Bin Zhang.

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Zhang, B., Wang, Q., Li, J. et al. Spatial–temporal grid clustering method based on frequent stay point recognition. Neural Comput & Applic 34, 9247–9255 (2022). https://doi.org/10.1007/s00521-021-06274-2

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  • DOI: https://doi.org/10.1007/s00521-021-06274-2

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