Enhancing Accessibility for Individuals With Limited Mobility: Leveraging AI and Cycling Data for an Inclusive Active Transportation Network

Details

• Creators:
  Thapa, Madhu Mausam ; Luitel, Sanjay ; Markovic, Nikola ; Rashidi, Abbas ; Chamberlin, Robert
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Utah. Department of Transportation. Research and Innovation Division ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Accessibility Artificial Intelligence Computer Vision Data Analysis Data Bike Defect Detection Defects Global Positioning System Government Agencies Machine Learning Mobility Nonmotorized Transportation Persons With Disabilities Spatial Analysis Trail Maintenance Trails Transportation Equity YOLOv8

  More +
• Publication/ Report Number:
  UT-26.02
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Utah
• Edition:
  Final Report: Sept 2024 to Oct 2025
• Corporate Publisher:
  Utah. Dept. of Transportation. Research Division
• Abstract:
  This study introduces a low-cost, scalable framework for monitoring and assessing the condition of paved trail infrastructure to enhance accessibility for all users, particularly those with limited mobility. The research utilizes a custom-built "Data Bike" with GPS and accelerometers embedded with high-resolution cameras to collect multimodal data along Utah's trail networks. A simple and intuitive data processing pipeline was developed that uses accelerometer-derived "jerk" events to automatically identify potential surface anomalies, triggering the extraction of corresponding image frames. A deep-learning object detection model (YOLOv8) was fine-tuned on a custom dataset and augmented with public imagery to detect and classify ten surface defects and obstructions, including various cracks, potholes, and vegetation obstacles. The model achieved promising results, with detections geolocated to enable spatial analysis and hotspot identification. This data-driven approach provides transportation agencies an objective and efficient tool to move from reactive to proactive maintenance, allowing for better resource allocation and systematic improvements to trail safety and accessibility. The project lays the groundwork for an integrated asset management system, including an interactive dashboard for visualizing trail conditions and prioritizing repairs.
• Format:
  PDF
• Funding:
  25-8243
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:eb41a310a26cbe4338243f970820de852c687691f1bdfc2bf4bebaf3492f19fd2438a2198e7dec186e0932e1042ce74c90aa810e030b475705f7829388d38626
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89921/dot_89921_DS1.pdf
• File Type:
  [PDF - 6.33 MB ]