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.

• Record URL:
  https://nitc.trec.pdx.edu/research/project/1269

Language

• English

Project

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

  NITC 1269

  69A3551747112

• 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

• TRT Terms: Accuracy; Bicycles; Data collection; Data fusion; Global Positioning System; Mobile applications; Smartphones; Traffic volume
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;

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