Generation of individual daily trajectories by GPT-2 - Mizuno, Takayuki; Fujimoto , Shouji; Ishikawa , Atushi (Frontiers in Physics, 2022)
We propose a new method to convert individual daily trajectories into token time series by applying the tokenizer ``SentencePiece'' to a geographic space divided using the Japan regional grid code ``JIS X0410.'' Furthermore, we build a highly accurate generator of individual daily trajectories by learning the token time series with the neural language model GPT-2. The model-generated individual daily trajectories reproduce five realistic properties: (1) the distribution of the hourly moving distance of the trajectories has a fat tail that follows a logarithmic function, (2) the autocorrelation function of the moving distance exhibits short-time memory, (3) a positive autocorrelation exists in the direction of moving for one hour in long-distance moving, (4) the final location is often near the initial location in each individual daily trajectory, and (5) the diffusion of people depends on the time scale of their moving.