Abstract

Understanding urban-scale building occupancy and commuting flow patterns is critical for sustainable city planning, energy efficiency, and transportation optimization. However, existing Urban Building Energy Modeling (UBEM) frameworks rely on standardized occupancy schedules that fail to capture spatial and temporal variations, leading to significant inaccuracies in energy consumption estimates.
This thesis introduces a data-driven approach to improve urban-scale occupancy estimation and commuting flow prediction by integrating mobility data with machine learning techniques. A Transportation-Informed Building Occupancy (TIBO) framework is developed to estimate dynamic building occupancy profiles using large-scale transportation datasets, including metro, bus, bike-sharing, and traffic flow data. TIBO-based occupancy profiles significantly enhance UBEM simulation accuracy compared to conventional ASHRAE schedules, reducing errors in energy demand predictions.
Since transportation-based occupancy estimation frameworks depend on transportation data as input, enhancing transportation demand and flow prediction improves building occupancy prediction. Therefore, this thesis presents a multi-task spatiotemporal deep learning framework for short-term bike-sharing demand prediction at the station level. By incorporating historical demand patterns, meteorological data, and a dynamically evolving semantic adjacency graph, the model jointly predicts bike pick-ups and drop-offs, addressing system imbalances.
Additionally, a novel geographic-semantic graph-based model for commuting flow prediction is introduced. By leveraging Graph Convolutional Networks and Graph Attention Networks, the model captures both geographic adjacency and semantic connectivity, providing precise estimation of urban mobility patterns. The proposed approach outperforms existing models, improving accuracy in forecasting commuting demand across city zones.
This research provides a holistic methodology for modeling urban dynamics, contributing to more accurate energy simulations, better-informed transportation planning, and smarter, sustainable cities.