Generating Individual Trajectories Using GPT-2 Trained from Scratch on
  Encoded Spatiotemporal Data
Taizo Horikomi Shouji Fujimoto Atushi Ishikawa Takayuki Mizuno
Following Mizuno, Fujimoto, and Ishikawa's research (Front. Phys. 2022), we
transpose geographical coordinates expressed in latitude and longitude into
distinctive location tokens that embody positions across varied spatial scales.
We encapsulate an individual daily trajectory as a sequence of tokens by adding
unique time interval tokens to the location tokens. Using the architecture of
an autoregressive language model, GPT-2, this sequence of tokens is trained
from scratch, allowing us to construct a deep learning model that sequentially
generates an individual daily trajectory. Environmental factors such as
meteorological conditions and individual attributes such as gender and age are
symbolized by unique special tokens, and by training these tokens and
trajectories on the GPT-2 architecture, we can generate trajectories that are
influenced by both environmental factors and individual attributes.