2024/05/24 by Xishun Liao, Liao, Xishun, Qinhua Jiang +8 · 6 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Generative grammar #Generative model #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2405.17468
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/05/24 · openalex created_date 2024/05/30 · openalex updated_date 2026/07/28
Human mobility plays a crucial role in transportation, urban planning, and public health. Advances in deep learning and the availability of diverse mobility data have transformed mobility modeling. However, existing deep learning models often focus on spatio-temporal patterns and struggle to capture the semantic interdependencies among activities, while also being limited by specific data sources. These challenges reduce their realism and adaptability. Traditional activity-based models (ABMs) face issues as well, relying on rigid assumptions and requiring extensive data, making them costly and difficult to adapt to new regions, especially those with limited conventional travel data. To address these limitations, we develop a novel generative deep learning approach for human mobility modeling and synthesis that incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to and accurately represent mobility patterns across diverse regions. The model is evaluated on a nationwide dataset of the United States, where it demonstrates superior performance in generating activity-location chains that closely follow ground truth distributions. Further tests using state- or city-specific datasets from California, Washington, and Mexico City confirm its transferability. This innovative approach offers substantial potential to advance mobility modeling research, particularly in generating synthetic human mobility data. This can provide urban planners and policymakers with enhanced tools for simulating mobility in diverse regions and better informing decisions related to transportation, urban development, and public health.