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Human Mobility Reimagined: Digital Twin Intelligence for Adaptive Campus Course Timetabling
arXiv:2503.06109v2 Announce Type: replace
Abstract: Daily operations in large campuses depend on how efficiently people \emph{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 including capacity, conflicts, feasibility must be satisfied alongside soft constraints including preferences, satisfaction, 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 integrates collaborative and content-based filtering with 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, improves classroom utilization, and enhances overall user satisfaction. 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.
Abstract: Daily operations in large campuses depend on how efficiently people \emph{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 including capacity, conflicts, feasibility must be satisfied alongside soft constraints including preferences, satisfaction, 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 integrates collaborative and content-based filtering with 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, improves classroom utilization, and enhances overall user satisfaction. 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.
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