Human Mobility Reimagined: Digital Twin Intelligence for Adaptive Campus Course Timetabling

ABSTRACT Daily operations in large campuses depend on how efficiently people move through space and time. In this sense, course timetables are more than administrative schedules: they act as mobility policies that orchestrate thousands of trajectories, shaping travel burden, congestion, accessibility, and the reliability of back‐to‐back transitions. Designing timetables that are both feasible and mobility‐friendly is challenging because hard constraints such as capacity, conflicts, and feasibility must be satisfied alongside soft constraints such as preferences, satisfaction, and coordination, all under dynamic conditions such as real‐time disruptions and evolving demand. Traditional static optimization methods often struggle to capture these human mobility impacts and to adapt when campus conditions change. This paper reconceptualizes course timetabling as a recommendation‐based task and leverages the Texas A&M Campus Digital Twin as a dynamic data platform to evaluate mobility consequences at scale. We propose an iterative framework that combines data‐driven candidate generation, multi‐criteria mobility‐aware scoring, and feedback‐driven refinement to generate ranked sets of adaptive timetable recommendations. A mobility‐aware composite scoring function combining classroom occupancy, travel distance, travel time, and vertical transitions systematically balances resource efficiency with human‐centered movement costs. Extensive experiments using real‐world data from Texas A&M University show that the proposed approach reduces mobility friction and travel inefficiencies and improves classroom utilization. By coupling recommendation‐oriented decision‐making with digital twin intelligence, this study provides a robust and scalable blueprint for mobility‐centered campus planning and resource allocation, with potential extensions to broader urban systems.