Exploring Data Fusion Techniques to Derive Bicycle Volumes on a Network

Planners and decision makers have increasingly voiced a need for network-wide estimates of bicycling activity. Such volume estimates have for decades informed motorized planning and analysis but have only recently become feasible for non-motorized travel modes. To date, the bulk of our information on bicycling activity has come from national and regional household travel surveys or observed counts of cyclists -- either short-duration manual or longer-term automated counts -- in a limited set of locations. Based on these datasets, models must be developed to assess network-wide conditions. Direct demand models have been estimated to explain observed counts as a function of surrounding land use, demographics, and other proxies of activity and cycling conditions. As an alternative, bicycle volumes can be predicted as part of a much more complex regional travel demand model, but in practice such models that include bicycling at a useful level of detail remain extremely rare. Recently, new sources of bicycling activity data have emerged. These derive primarily from GPS-based smartphone apps (e.g. Strava, Ride Report, Map My Ride) and GPS-enabled public bicycle sharing systems. These emerging data sources have potential advantages as a complement to traditional count data, and have even been proposed as replacements for such data, since they are collected continuously and for larger portions of local bicycle networks. However, the representativeness of these new data sources has been questioned, and their suitability for producing bicycle volume estimates has yet to be rigorously explored. The research proposed here would develop a method for evaluating and integrating emerging sources of bicycle activity data with conventional demand data and methods, and then apply the results to several locations to predict network-wide bicycle volumes. Anticipated outcomes include: (1) literature review, catalog, and evaluation of available third-party data sources and existing applications; (2) demonstration of bicycle volume models that incorporate emerging data sources; (3) comparison of the relative accuracy and value added by different data sources and modeling techniques; and (4) openly available scripts and documentation to help others evaluate, process, and apply emerging data sources for network-wide bicycle volume estimation.


  • English


  • Status: Active
  • Funding: $400000
  • Contract Numbers:

    NITC 1269


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590

    Oregon Department of Transportation

    555 13th Street NE
    Salem, OR  United States  97301

    Virginia Department of Transportation

    1221 East Broad Street
    Richmond, VA  United States  23219

    Colorado Department of Transportation

    4201 East Arkansas Avenue
    Denver, CO  United States  80222

    Central Lane MPO

    859 Willamette St Suite 500
    Eugene, Oregon  United States  97401

    Portland Bureau of Transportation

    1120 SW 5th Ave
    Suite 800
    Portland, Oregon  United States  97201

    District Department of Transportation

    250 M Street, SE
    Washington, DC  United States  20003

    Utah Department of Transportation

    4501 South 2700 West
    Project Development
    Salt Lake City, UT  United States  84114-8380
  • Managing Organizations:

    TREC at Portland State University

    1900 SW Fourth Ave, Suite 175
    P.O. Box 751
    Portland, Oregon  United States  97201
  • Project Managers:

    Hagedorn, Hau

  • Performing Organizations:

    Portland State University

    Department of Civil and Environmental Engineering
    Engineering Bldg, 301D, 1930 SW 4th Ave.
    Portland, OR  United States  97201

    University of Texas at Arlington

    Department of Civil Engineering
    Box 19308
    Arlington, TX  United States  76019
  • Principal Investigators:

    Kothuri, Sirisha

    Hyun, Kate

    Broach, Joseph

  • Start Date: 20190101
  • Expected Completion Date: 20220331
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01689557
  • Record Type: Research project
  • Source Agency: National Institute for Transportation and Communities
  • Contract Numbers: NITC 1269, 69A3551747112
  • Files: UTC, RIP
  • Created Date: Dec 21 2018 4:57AM