Data Acquisition and Processing Using Artificial Intelligence and Machine Learning

Details

• Creators:
  Loos, Ryan ; Egge, Mark ; Batista, Anna
• Corporate Creators:
  High Street Consulting Group (HSCG)
• Corporate Contributors:
  Missouri. Department of Transportation. Construction and Materials Division ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Artificial intelligence
  • Machine learning
  • Computer vision
  • Automation
  • Data collection
  • Data analysis Deep learning
  • Factor grouping
  • Median inventory
  • Advance analytics
  • Automated data collection
• Publication/ Report Number:
  cmr 24-009
• Resource Type:
  Tech Report
• Geographical Coverage:
  Missouri;United States
• Edition:
  Final Report (June 2022-June 2024)
• Corporate Publisher:
  Missouri. Department of Transportation. Construction and Materials Division
• Abstract:
  Artificial intelligence (AI) and machine learning (ML) offer state transportation agencies promising tools to help them advance their missions by informing decision making; improving information accuracy, completeness, and timeliness; and automating tedious tasks to free up valuable DOT resources. The objective of this research was to provide MoDOT with tools, information, and examples to implement and leverage these technologies. The project reviewed existing work efforts and explored opportunities where AI and ML could replace existing work activities or augment and improve them. This was completed by developing a universe of potential DOT applications, screening them based on key criteria, scoping five potential projects, and executing two pilot projects. Findings from the idea generation process and execution of the pilots can be applied to MoDOT’s future AI and ML endeavors.
• Format:
  PDF
• Funding:
  MoDOT project # TR202215
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/76741/dot_76741_DS1.pdf
• File Type:
  [PDF - 2.97 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:dedb6317ef8898aa1fa453c23854a06ba15e9ff367021038f132c16413eae5ceda77c0d719df978b7e136b697c069c90835e56c1b583149b2968452174fa6cd6