Deriving Transportation Mode Shares on Urban Freeways Based on Mobile Phone Data

An innovative method is presented in this paper to derive the transportation mode shares on urban freeways using mobile-phone trajectory information. It consists of two major parts: offline learning and online inference. The offline learning first extracts the temporal feature from the mobile-phone trajectories. By comparing to the existed link volumes, the inference parameters are calibrated through the offline learning process. The online inference determines the transportation modes for each individual mobile phone users in a real-time manner. The methodology was tested via a case study designed for both the offline learning and online inference parts. The results show the great potential of using mobile-phone trajectory information as a means to estimating the transportation mode shares.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p
  • Monograph Title: 18th ITS World Congress, Orlando, 2011. Proceedings

Subject/Index Terms

Filing Info

  • Accession Number: 01487115
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 2 2013 8:28AM