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TraceBERT-A Feasibility Study on Reconstructing Spatial-Temporal Gaps from Incomplete Motion Trajectories via BERT

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TraceBERT reconstructs missing trajectory data by adapting language models like BERT. This approach effectively fills spatial-temporal gaps in human mobility patterns, outperforming traditional methods.

Keywords:
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Area of Science:

  • Mobility Data Science
  • Geographic Information Science
  • Artificial Intelligence

Background:

  • Trajectory data are crucial for understanding human mobility and travel behaviors.
  • Data collection issues often lead to spatial-temporal gaps and missing trajectory segments.
  • Reconstructing non-repetitive individual motion traces is challenging due to the lack of historical data.

Purpose of the Study:

  • To address the problem of missing trajectory data by proposing a novel framework.
  • To adapt advanced natural language processing techniques for trajectory reconstruction.
  • To evaluate the effectiveness of the proposed method on real-world mobility data.

Main Methods:

  • Leveraging Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language representation model.
  • Training deep bidirectional representations from unlabeled location sequences, conditioned on both left and right context.
  • Developing a framework named TraceBERT for trajectory processing and reconstruction.

Main Results:

  • TraceBERT demonstrated effective trajectory reconstruction by predicting missing locations.
  • The framework was tested on a large-scale real-world dataset of short-term tourist trajectories.
  • The proposed method showed prominent potential compared to traditional statistical approaches.

Conclusions:

  • Advanced language modeling approaches can be successfully adapted for mobility-based applications.
  • TraceBERT offers a promising solution for filling spatial-temporal gaps in trajectory data.
  • The study highlights the potential of AI in enhancing the analysis of human mobility patterns.