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April 6, 2026Journal of Advanced TransportationOpen Access

Uncovering Regular Travel Pattern for Metro Passenger Through Graph Neural Network

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Authors

XLXinyun LiangJCJingjing ChenYWYi Wang

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Overview

Demonstrates a method to identify travel patterns in urban rail transit, suggesting improved service efficiency for metro operators.

Key Points

  • The study aims to understand complex passenger travel patterns in urban rail systems using advanced data analysis.
  • Utilized Automated Fare Collection data
  • Constructed spatiotemporal travel graphs
  • Employed an enhanced GraphSAGE model
  • Introduced a weighted aggregation strategy
  • Clustered passengers into four groups based on travel behaviors
  • Successfully identified regular and distinct travel patterns among passengers
  • Showed the effectiveness of the method through a real-world case study at Anting Station
  • Provided practical insights for improving operational efficiency and service quality

Cite This Study

Liang et al. (2026) studied this question.

synapsesocial.com/papers/69d34e1e9c07852e0af97a3fhttps://doi.org/10.1155/atr/6820472
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