Work

Modeling and generating human mobility trajectories using transformer with day encoding

Year: 2023

Type: conference-paper

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.

DOI: 10.1145/3615894.3628506

Authors: Akihiro Kobayashi, Naoto Takeda, Yudai Yamazaki, Daisuke Kamisaka

Institution: KDDI Research (Japan)

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Funders: Ministry of Internal Affairs and Communications, National Institute of Informatics

Award: G7222400979