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A geometry-driven neural topic model for trip purpose inference

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Abstract

Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes cannot be directly extracted from the trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby POI data using a geometry-driven technique to enhance the interpretability of the results. Furthermore, our model captures the semantics and relationships of the data in a high-dimensional space and identifies latent topics representing distinct trip purposes. These learned topics are analyzed using clustering algorithms to group similar trips, enabling trip purpose inference. And we evaluate our model using the trajectory data of Shenzhen and Chengdu, and compare it with baseline models. The results demonstrate that our model performs well. Furthermore, we analyze trajectory data containing trip purpose information to gain insight into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions.

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Funding

This work was partially supported by the Grants of National Key Research and Development Program of China (2021YFB1714400), Guangdong Provincial Key Laboratory (2020B121201001), and the Grant in-Aid for Scientific Research B (22H03573) of Japan Society for the Promotion of Science (JSPS)

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Authors and Affiliations

  1. SUSTech-UTokyo Joint Research Center for Super Smart Cities, Department of Computer Science and Engineering, Southern University of Science and Technology, ShenZhen, Guangdong, China

    Jiaqi Zhang, Zipei Fan, Xuan Song & Ryosuke Shibasaki

  2. Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

    Zipei Fan & Ryosuke Shibasaki

Authors
  1. Jiaqi Zhang
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  2. Zipei Fan
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  3. Xuan Song
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  4. Ryosuke Shibasaki
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Contributions

Jiaqi Zhang and Zipei Fan wrote the main manuscript text. All authors reviewed the manuscript

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Correspondence to Zipei Fan or Xuan Song.

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Zhang, J., Fan, Z., Song, X. et al. A geometry-driven neural topic model for trip purpose inference. Geoinformatica 28, 313–333 (2024). https://doi.org/10.1007/s10707-023-00504-6

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