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How do you visit: Identifying addicts from large-scale transit records via scenario deep embedding

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Abstract

Identification of individuals based on transit modes is of great importance in user tracking systems. However, identifying users in real-life studies is not trivial owing to the following challenges: 1) activity data containing both temporal and spatial context are high-order and sparse; 2) traditional two-step classifiers depend on trajectory patterns as input features, which limits accuracy especially in the case of scattered and diverse data; 3) in some cases, there are few positive instances and they are difficult to detect. Therefore, approaches involving statistics-based or trajectory-based features do not work effectively. Deep learning methods also suffer from the problem of how to represent trajectory vectors for user classification. Here, we propose a novel end-to-end scenario-based deep learning method to address these challenges, based on the observation that individuals may visit the same place for different reasons. We first define a scenario using critical places and related trajectories. Next, we embed scenarios via path-based or graph-based approaches using extended embedding techniques. Finally, a two-level convolution neural network is constructed for the classification. Our model is applied to the problem of detection of addicts using transit records directly without feature engineering, based on real-life data collected from mobile devices. Based on constructed scenario with dense trajectories, our model outperforms classical classification approaches, anomaly detection methods, state-of-the-art sequential deep learning models, and graph neural networks. Moreover, we provide statistical analyses and intuitiveexplanations to interpret the characteristics of resident and addict mobility. Our method could be generalized to other trajectory-related tasks involving scattered and diverse data.

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Notes

  1. https://www.fs.fed.us/pnw/starkey/data/tables/

  2. https://github.com/jincanghong/traj_cls/tree/master

  3. https://github.com/scikit-learn-contrib/imbalanced-learn

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Acknowledgements

Our research is supported by the Natural Science Foundation of Zhejiang Province of China under Grant (No. LY21F020003), Zhejiang Provincial Key Research and Development Program of China (NO. 2021C01164).

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Authors and Affiliations

  1. Zhejiang University City College, 51 Huzhou street, 310015, Hangzhou, China

    Canghong Jin & Minghui Wu

  2. Dartmouth College Hanover, Hanover, NH, 03755, USA

    Dongkai Chen

  3. Zhejiang University, 310027, Hangzhou, China

    Zhiwei Lin

  4. Singapore Management University, 81 Victoria Street, Singapore, 188065, Singapore

    Zemin Liu

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  1. Canghong Jin
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  2. Dongkai Chen
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  3. Zhiwei Lin
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  4. Zemin Liu
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  5. Minghui Wu
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Correspondence to Minghui Wu.

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Jin, C., Chen, D., Lin, Z. et al. How do you visit: Identifying addicts from large-scale transit records via scenario deep embedding. Geoinformatica 25, 799–820 (2021). https://doi.org/10.1007/s10707-021-00448-9

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