Generating Individual Trajectories using the Autoregressive Language Model

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  • 自己回帰型言語モデルによる個人の移動軌跡の生成

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<p>We construct a pre-learning model for individual daily trajectories by inputting travel time and travel location into GPT-2, an autoregressive language model, utilizing the location history of approximately 680,000 smartphones that traversed Urayasu city in August 2022. Additionally, we incorporate environmental factors, such as weather conditions and daily new coronavirus cases, as well as attribute information of the smartphone owners. During the learning process in the model, numerical information is transformed into unique character combinations. By this transformation, we can obtain highly accurate individual daily trajectory models without the need for geographic information.</p>

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