[2H5-OS-8a] 移動系列のデータマイニングと機械学習

[2H5-OS-8a-02] Generating Individual Trajectories using the Autoregressive Language Model

〇Takayuki Mizuno^1,2, Taizo Horikomi², Shouji Fujimoto³, Atushi Ishikawa³ (1. National Institute of Informatics, 2. The Graduate University for Advanced Studies, SOKENDAI, 3. Kanazawa Gakuin University)

Keywords:individual daily trajectories, trajectory generation, mobility model, GPT-2

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.