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

TL;DR

The paper reframes university course timetabling as a mobility-aware resource allocation problem managed via a campus digital twin. It introduces an iterative, hybrid filtering recommendation framework that jointly optimizes classroom utilization and student movement costs through a composite mobility score. Across real TAM data, the approach reduces travel distance and time, lowers vertical movement burdens, and improves occupancy balance while satisfying hard constraints, demonstrating portability to broader urban systems. The work emphasizes adaptability, transparency, and human-centric mobility outcomes, offering a scalable blueprint for mobility-conscious campus planning and resource allocation.

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.

Figures (11)

• Figure 1: Texas A&M Digital Twin.
• Figure 2: Overview of the Optimization Problem.
• Figure 3: Highlight 1: Spatial-Temporal Factors and Correlations and Highlight 2: Multi-Modal Transportation Options.
• Figure 4: Overview of the Iterative Optimization Process.
• Figure 5: Detailed Steps of the Iterative Optimization Process.