AI-Driven Road Management

This course equips transportation professionals with the knowledge and skills to transform road asset management using artificial intelligence (AI) and computer vision (CV). Participants explore how AI-driven systems enable predictive maintenance, optimize resource allocation, and support data-driven decisions for road networks.

Through a blend of lectures, case studies, and hands-on labs, attendees learn how to define functional and non-functional requirements, select appropriate AI models, and craft clear specifications for AI-based road management solutions. The course is vendor-neutral and focuses on universal principles and technologies applicable to any Department of Transportation (DoT), enabling participants to envision and implement AI solutions tailored to their organization’s needs.

By the end of this course, participants will be able to:

• Understand core AI and computer vision concepts as they apply to road inspection and asset management
• Identify the roles of data, human oversight, and technology in AI-driven road management systems
• Define essential functional and non-functional requirements for AI-based road management solutions
• Establish meaningful performance metrics and data requirements to ensure system reliability
• Develop a structured specification framework for describing and procuring an AI-driven road management plan
• Evaluate AI solutions for fairness, robustness, and integration with existing DoT systems

This course is designed for transportation and infrastructure professionals involved in planning, managing, or overseeing road assets and maintenance programs, including:

• DoT engineers and planners
• Asset and infrastructure managers
• Transportation program and project managers
• Data and GIS professionals
• Policy makers and technical decision-makers involved in road management systems

It is suitable for both technical and non-technical participants who are responsible for evaluating, specifying, or procuring AI-driven road management solutions.

While the course is designed to be accessible to participants with varying experience levels, having some technical familiarity will enhance the learning experience. A basic understanding of transportation systems, data concepts, or infrastructure management will help attendees engage more effectively with hands-on activities and advanced topics, but no deep AI or programming background is required.