2026 ASEE Annual Conference & Exposition

The rapid advancement of artificial intelligence (AI) is reshaping transportation engineering (TE), creating demand for a new generation of engineers who can integrate AI tools into the design, operation, and management of transportation systems. While universities and industry training providers have begun to explore AI applications in TE, there is limited understanding of the current state of AI-related education, the extent of adoption, and the directions of future development. This study addresses these gaps by conducting a structured review of TE curricula and training programs in the United States, with a focus on how AI concepts, methods, and skills are being introduced.
A mixed-method research approach was designed to combine quantitative and qualitative analyses. First, a curriculum survey compiled a database of TE programs and courses using publicly available resources such as catalogs and syllabi, focusing on keywords including “AI,” “machine learning,” “data science,” “autonomous vehicles,” and “intelligent transportation systems (ITS).” Second, content analysis classified instructional approaches and themes, such as AI fundamentals, AI-enhanced traffic modeling, decision support for transportation planning, and ethical considerations. Third, training programs from organizations such as the Institute of Transportation Engineers (ITE) and the Transportation Research Board (TRB) were reviewed to capture how AI was introduced to practicing transportation professionals. Finally, classification and predictive models were applied to categorize offerings, identify patterns, and forecast trends in AI-integrated TE education. This study is relevant to both academia and industry. For academic institutions, it provides evidence-based insights to inform curriculum design and strategic planning. For industry, it highlights professional development strategies aligned with evolving standards.