Exploring Crowdsourced Monitoring Data for Safety - Evaluation of Miovision Pedestrian Count Data [supporting datasets]

Details

• Creators:
  Le, Minh
• Corporate Creators:
  Safety through Disruption (Safe-D) University Transportation Center (UTC)
• 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 +
• DOI:
  https://doi.org/10.15787/VTT1/351GZJ
• 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 data represent one week of selected hourly weekday and weekend pedestrian counts at two intersections in Austin, Texas. The pedestrian counts were produced from manually reducing video files from Miovision’s TrafficLink Multimodal Detection and Counts system. Eighty hours of video were gathered at each intersection between June 18 and July 14, 2019. However, only 40 hours at each intersection were reduced and evaluated. The manual counts were compared to Miovision’s count data across different combinations of lighting conditions and pedestrian volumes. Overall, Miovision system performed fairly well with accuracy results of 15% error for daytime and 24% for nighttime for the combined intersection legs.
• Format:
  ZIP
• Funding:
  69A3551747115/TTI-Student-05
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/54626/dot_54626_DS1.pdf
• File Type:
  [PDF - 156.66 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:62bb235547154575a0d8abfd8dc0baea4c1996961afd0885d4ade0e8c98d663f65824727757adca3f38045a69f900bd9d2bea7fd8f781ba69f863d9b6f931feb