Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management [Research Brief]

Details

• Creators:
  Chen, Jianli ; Kuang, Biao
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering ; Upper Great Plains Transportation Institute
• 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:
  • Artificial intelligence
  • Asset management
  • Computer vision
  • Data collection
  • Global Positioning System
  • Highway maintenance
  • Mobile applications
  • Smartphones
  • State departments of transportation
  • Literature reviews
• Publication/ Report Number:
  MPC 24-533
• Resource Type:
  Brief
• Geographical Coverage:
  Utah ; United States
• Edition:
  Research Brief
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  To achieve the research objectives of this project, four specific tasks are involved: Task 1: A comprehensive literature review is conducted to explore and examine existing technologies and practices related to transportation asset collection and inspection, including emerging AI technologies. Task 2: A mobile phone mounted on a vehicle is used to collect data by recording videos while driving on Utah highways and streets. The capability of AI in transportation asset identification and condition assessment is pre-evaluated. Task 3: Based on self-collected images and utilizing AI algorithms, multiple AI models are developed to inspect and identify transportation assets, including assessing pavement marking conditions, identifying the various traffic signs, and detecting common litter on the roads. The performance of each model is evaluated. Task 4: An AI prototype model is also developed to identify concrete barriers and steel guardrails.
• Format:
  PDF
• Funding:
  668
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/77213/dot_77213_DS1.pdf
• File Type:
  [PDF - 542.67 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3a3b3dddabef57cf2f672e4f9604986bad35a09b71f7fc9525350afabc54b9a013d6b066d3b207ac2dd1388caa5b0cb64a251ea578c660fa16753daefa52c3e4