Modeling Transit Patterns Via Mobile App Logs

Transit planners need detailed information of the trips people take using public transit in order to design more optimal routes, address new construction projects, and address the constantly changing needs of a city and metro region. Unfortunately, good rider origin-destination (O-D) information is almost universally unavailable. The goal of this project is to develop machine-learning models that can infer O-D for a transit service based on the request logs of individual users of mobile transit apps. This project builds on already deployed and extensively used Tiramisu app. The project attempts to generate a generic model that takes Tiramisu mobile app data as input and outputs a highly accurate and generic travel model for the transit community using the app. The approach adopted in this project combines raw Tiramisu data with common sense assumptions to address questions about commuter behavior. The authors build statistical models and provide visualization of commuter behavior, which helps identify common behavioral patterns, inefficient routes, under served routes and predict the likely destination.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Research Report
  • Features: Figures; Maps;
  • Pagination: 8p

Subject/Index Terms

Filing Info

  • Accession Number: 01603574
  • Record Type: Publication
  • Contract Numbers: DTRT12GUTG11
  • Files: UTC, NTL, TRIS, RITA, ATRI, USDOT
  • Created Date: May 27 2016 9:53AM