Using Computer Vision Devices to Evaluate Bicycle and Pedestrian Improvements

Details

• Creators:
  Malarkey, Daniel ; Singh, Rubina ; MacKenzie, Don ; Lowry, Michael ; Thompson, Matthew
• Corporate Creators:
  University of Washington ; Pacific Northwest Transportation Consortium (PacTrans) (UTC) ; University of Idaho
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Bicycles
  • Safety
  • Pedestrians
  • Computer vision
  • Multimodal transportation
  • Bicycle lanes
  • Evaluation
  • Walking
  • computer vision
  • multimodal design
  More +
• Publication/ Report Number:
  2022-M-UW-1
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report January 2022-March 2024
• Corporate Publisher:
  Pacific Northwest Transportation Consortium (PacTrans) (UTC)
• Abstract:
  The design of urban streets and sidewalks must balance the mobility and safety requirements of pedestrians, cyclists, transit users, freight vehicles, and auto drivers. Modern engineers and designers supplement traditional traffic engineering approaches that focus on metrics for automobiles and trucks with more formal consideration of people traveling by foot, bicycle, and bus. The advent of computer vision systems that record counts and pathways of all street and sidewalk users has created new opportunities for data collection, supporting insights that are not possible from the pneumatic tube counters or electromagnetic sensors that are commonly used to measure car and truck traffic. The resulting data can inform street designs that better accommodate all travelers and modes with greater safety. However, computer vision systems using emerging technology must demonstrate their ability to generate consistent, valid, and actionable data before their widespread adoption by urban designers and engineers.
• Format:
  PDF
• Funding:
  69A3551747110
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/79647/dot_79647_DS1.pdf
• File Type:
  [PDF - 1.60 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:f96d07d6a7f823e496c20ecb87cec77dc2fee43f9aab8a3d61ac2a26d2c4ae57873e2a7c7e7a8bf096a10315e9ba861b998ad3b44caedd34b9623c72a7f1f8cf