Exploring Crowdsourced Monitoring Data for Safety

Details

• Creators:
  Turner, Shawn ; Martin, Michael ; Griffin, Greg ; Le, Minh ; Das, Subasish ; Wang, Ruihong ; Dadashova, Bahar ; Li, Xiao
• Corporate Publisher:
  Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A&M Transportation Institute ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Accuracy
  • Bicycle counts
  • Crash data
  • Crash risk forecasting
  • Crowdsourcing
  • Data analysis
  • High risk locations
  • Origin and destination
  • Pedestrian counts
  • Travel patterns Data and Information Technology
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  Texas
• Description:
  This project included four distinct but related exploratory studies of data sources that could improve roadway safety analysis. The first effort evaluated passively gathered crowdsourced bicyclist activity data from StreetLight Data and found promising correlations (R2 of 62% and 69% for monthly weekday and weekend daily averages) when the StreetLight data were compared to bicyclist counts from 32 locations in eight Texas cities, and even better correlation (R2 of 94%) when compared with countywide Strava data expanded to represent total bicycling activity. The second effort evaluated the pedestrian counting accuracy of the Miovision system and found 15% error for daytime and 24% error for nighttime conditions. The third effort used INRIX trip trace data to determine origin-destination patterns and developed 40 decision rules to define the origin-destination patterns. The fourth effort analyzed crowdsourced Waze data (i.e., traffic incidents) and found it to be a reliable alternative to observed and predicted crashes, with the ability to identify high-risk locations: 77% of high-risk locations identified from police-reported crashes were also identified as high-risk in Waze data. The researchers propose a method to treat the redundant Waze reports and to match the unique Waze incidents with police crash reports.
• Format:
  PDF
• Funding:
  69A3551747115/TTI-Student-05
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/50717/dot_50717_DS1.pdf
• File Type:
  [PDF - 13.29 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:1422ce5726c4229481b961b58b2517016b620462eea1577319c8f3a350d04d1c478fae1dbb0214e3f9768e44c95a1ada73219b57a2bdcf8912cdb003102764e8