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.

将地理坐标转化为代表不同空间尺度位置的特殊标记，并通过自回归语言模型GPT-2对这些标记和轨迹进行训练，构建了一个受环境因素和个体属性影响的深度学习模型，能够生成个人每日轨迹。

使用从头开始训练的GPT-2生成基于编码时空数据的个体轨迹