Artificial Intelligence in Transportation

Details

• Creators:
  Chen, Sikai ; Li, Pei ; Sheng, Zihao ; Ma, Junyi ; Cheng, Yang ; Qin, Xiao ; Li, Yang ; Shi, Tom ; Roberts, Joel
• Corporate Creators:
  University of Wisconsin--Madison. Department of Civil and Environmental Engineering ; University of Wisconsin--Milwaukee
• Corporate Contributors:
  Wisconsin. Dept. of Transportation. Library and Research Unit ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Artificial Intelligence Asset Management Autonomous Vehicles Data Collection Digital Twins Generative AI Highway Operations Interviewing Surveys Transportation Transportation Safety Workforce

  More +
• Publication/ Report Number:
  0092-24-14
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: August 7, 2025 - July 31, 2025
• Corporate Publisher:
  Wisconsin. Dept. of Transportation. Library and Research Unit
• Abstract:
  This research provides a strategic assessment of how Artificial Intelligence (AI) can be effectively integrated into the operations of the Wisconsin Department of Transportation (WisDOT). The purpose of the research is to identify key opportunities, challenges, and implementation pathways for AI across six major transportation domains: asset management, safety, operations, digital twin, autonomous vehicles, and generative AI. The research employed a mixed-methods approach, including a comprehensive literature review, a nationwide stakeholder survey, and expert interviews with professionals from public agencies and industry. The findings reveal substantial differences in AI perception between agency and non-agency stakeholders, highlight asset management and operations as near-term priorities, and identify critical barriers such as data fragmentation and workforce readiness. The research introduces a phased AI implementation roadmap and offers tailored recommendations under three time periods: short-term, medium-term, and long-term. These results provide recommendations to WisDOT in prioritizing high-impact, low-risk AI applications in the short term, while laying the groundwork for advanced, system-wide AI integration in the future.
• Format:
  PDF
• Funding:
  0092-24-14
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:084367e22e88e4e0f6f08078e4a9c4e68464ae9f9f1eca700ed897fbbc9274683e4d36e151468a6a622b0ecb12b31f8643afb11f9122effda586c1fb908995c0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89224/dot_89224_DS1.pdf
• File Type:
  [PDF - 3.09 MB ]