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Following Mizuno, Fujimoto, and Ishikawa's research (Front. Phys. 2022), wetranspose geographical coordinates expressed in latitude and longitude intodistinctive location tokens that embody positions across varied spatial scales.We encapsulate an individual daily trajectory as a sequence of tokens by addingunique time interval tokens to the location tokens. Using the architecture ofan autoregressive language model, GPT-2, this sequence of tokens is trainedfrom scratch, allowing us to construct a deep learning model that sequentiallygenerates an individual daily trajectory. Environmental factors such asmeteorological conditions and individual attributes such as gender and age aresymbolized by unique special tokens, and by training these tokens andtrajectories on the GPT-2 architecture, we can generate trajectories that areinfluenced by both environmental factors and individual attributes.

Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data