Modeling and generating human mobility trajectories using transformer with day encoding

Abstract

Modeling and predicting human mobility trajectories in urban areas is a crucial challenge with various applications. The HuMob Challenge is a competition that focuses on modeling human mobility trajectories using large open-source datasets. This paper presents one of the top 10 winning methods from the competition. In recent years, a method has been developed to apply advanced large language model (LLM) technology to model human movement. We have enhanced trajectory prediction accuracy by encoding daily characteristics, such as commuting to the office on weekdays and visiting shopping districts on weekends, to capture day-to-day variations in movement patterns.

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