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Data Sharing Guidance for Public Transit Agencies—Now and in the Future (2020) / Chapter Skim
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Appendix A - Data Types and Sharing Attributes
Pages 61-65

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From page 61...
... Using TfL data for a 4-week period, researchers clustered transit users based on their activity patterns. They found that 40% of frequent transit users did not follow a conventional trip activity sequence involving one trip to work in the morning and another trip home in the evening (Goulet-Langlois et al.
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From page 62...
... . Like fare card transaction data, the fact that this data tracks individual devices means that there are some privacy risks associated with sharing the data in disaggregate form.
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From page 63...
... In rare cases, incident data may pose privacy risks if individuals involved in the incidents are described in an identifiable way. One transit agency interviewee noted that their agency does not release incident data publicly because of the staff effort that would be required to read descriptive data fields to confirm that they could not be used to identify individual passengers.
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From page 64...
... In the area of crew scheduling, there was considerable research in the past, but there are now off-the-shelf solutions that transit agencies use. One transit agency interviewee indicated their agency provided operations data to a research partner who helped them pilot a new bus operations method.
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From page 65...
... Transportation Planning App Data Transportation planning apps include navigation apps, such as Google Maps and Waze, and apps such as Transit App and NextBus, that provide information on transit vehicle arrivals and collect information including the following: • Records for each session, including beginning and ending coordinates and time stamps • Placemarks -- stored home and work locations • Carshare, bikeshare, and TNC bookings (if available through the app) • Trip planning routes, stops searched, and favorite routes Data from these apps provides an additional layer of insight about other location data from smartphone apps.
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Key Terms

  • transit agency
  • public transit
  • privacy risks
  • maintenance data
  • card data

  • data sharing
  • data types
  • agency interviewee
  • trip planning
  • transit users

  • incident data
  • app data
  • avl data
  • agency interviewees
  • interviewee indicated

  • smart card
  • data transit
  • transit data
  • transit station
  • helps transit

  • transit vehicle
  • bikeshare data
  • financial data
  • data includes
  • operations data

  • agencies share
  • agency spending
  • data tracks
  • data publicly
  • demand patterns


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