Feb. 6, 2024, 5:44 a.m. | Ammar Haydari Dongjie Chen Zhengfeng Lai Chen-Nee Chuah

cs.LG updates on arXiv.org arxiv.org

Generative models have shown promising results in capturing human mobility characteristics and generating synthetic trajectories. However, it remains challenging to ensure that the generated geospatial mobility data is semantically realistic, including consistent location sequences, and reflects real-world characteristics, such as constraining on geospatial limits. To address these issues, we reformat human mobility modeling as an autoregressive generation task, leveraging Generative Pre-trained Transformer (GPT). To ensure its controllable generation to alleviate the above challenges, we propose a geospatially-aware generative model, MobilityGPT. …

consistent cs.lg data generated generative generative models geospatial gpt human location mobility modeling synthetic world

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