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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.06109 |
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| _version_ | 1866918248535031808 |
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| author | Wu, Keshu Ye, Xinyue Jamonnak, Suphanut Feng, Xin |
| author_facet | Wu, Keshu Ye, Xinyue Jamonnak, Suphanut Feng, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human Mobility Reimagined: Digital Twin Intelligence for Adaptive Campus Course Timetabling Wu, Keshu Ye, Xinyue Jamonnak, Suphanut Feng, Xin Computational Engineering, Finance, and Science 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. |
| title | Human Mobility Reimagined: Digital Twin Intelligence for Adaptive Campus Course Timetabling |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2503.06109 |