Urban Trajectory Analytics: Day-of-Week Movement Pattern Mining Using Tensor Factorization

This paper demonstrates the use of non-negative tensor factorization to extract underlying spatio-temporal movement patterns from large-scale urban trajectory data. Individual trajectory data obtained from public transport smart card systems and roadside Bluetooth detectors are represented as a dynamic graph of region-to-region flows to obtain structured data describing flow interactions between regions across time-of-day and day-of-week. Tensor factorization is then applied to these dynamic graphs to characterize traveler movements on different days of the week. The results unveil distinct day-of-week patterns in public transport passenger and roadway vehicle movements, providing insight into the diverse aspects of urban mobility.

Language

  • English

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Filing Info

  • Accession Number: 01713037
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Jun 28 2019 12:11PM