INTEGRATING CUTTING-EDGE PRESCRIPTIVE ANALYTICS INTO TRANSPORTATION EDUCATION: BRIDGING RESEARCH, APPLICATION, AND CRITICAL THINKING

The rapid development of prescriptive analytics (PA), driven by machine learning (ML) and optimization methodologies, has notably influenced transportation research and practical operations. Recognizing the pivotal role of big data analytics in contemporary transportation decision-making, this paper presents the philosophy and instructional strategy underlying a specialized course aimed at equipping research students with advanced PA tools to tackle real-world transportation challenges. Unlike traditional curricula that depend primarily on textbooks and theoretical concepts, this course emphasizes the latest advancements in applying data-driven optimization techniques within transportation analytics. The curriculum specifically covers modern topics, including enhancements in traditional ML, distributional estimation techniques, and end-to-end learning frameworks. Instructional practices intentionally embrace the perspectives of researchers and reviewers, guiding students in identifying frequent methodological pitfalls, correcting errors in mathematical exposition, and critically evaluating existing scholarly literature. Through this curriculum, students learn to translate theoretical insights from statistics and operations research into actionable transportation solutions.