Exploring Crowdsourced Monitoring Data for Safety - Travel Patterns using GPS Waypoint Data [supporting datasets]

Details

• Creators:
  Das, Subasish ; Wang, Ruihong
• Corporate Creators:
  Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A & M Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; Texas (State)
• Subject/TRT Terms:
  • Accuracy
  • Bicycle counts
  • Crash data
  • Crash risk forecasting
  • Crowdsourcing
  • Data analysis
  • High risk locations
  • Origin and destination
  • Pedestrian counts
  • Travel patterns
  More +
• DOI:
  https://doi.org/10.15787/VTT1/81SKJW
• Resource Type:
  Dataset
• Geographical Coverage:
  Texas
• 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
• Abstract:
  The third effort (Travel Patterns using GPS Waypoint Data) of this project explored the use of INRIX trip trace data to determine the patterns of origin-destination distributions and developed forty decision rules to define the origin-destination patterns. The research team used three separate databases to perform the analysis: Maryland INRIX waypoint trip data, Census block group level American Community Survey (ACS) 2013-2017 data, and Census block level Longitudinal Employer-Household Dynamics (LEHD) 2015 data.
• Format:
  ZIP
• Funding:
  69A3551747115/TTI-Student-05
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/53577/dot_53577_DS1.pdf
• File Type:
  [PDF - 157.85 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:03d34cf74a230e847c0c0b7b0f5f553df99864eb82e2b1aae8ddb882ea8e0b984d02fe28aa3f72347a957ffba30a635ca90916f0e00a45d535e6cf66720fb0ad