Back to Search Start Over

Learning Daily Human Mobility with a Transformer-Based Model

Authors :
Weiying Wang
Toshihiro Osaragi
Source :
ISPRS International Journal of Geo-Information, Vol 13, Iss 2, p 35 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The generation and prediction of daily human mobility patterns have raised significant interest in many scientific disciplines. Using various data sources, previous studies have examined several deep learning frameworks, such as the RNN and GAN, to synthesize human movements. Transformer models have been used frequently for image analysis and language processing, while the applications of these models on human mobility are limited. In this study, we construct a transformer model, including a self-attention-based embedding component and a Generative Pre-trained Transformer component, to learn daily movements. The embedding component takes regional attributes as input and learns regional relationships to output vector representations for locations, enabling the second component to generate different mobility patterns for various scenarios. The proposed model shows satisfactory performance for generating and predicting human mobilities, superior to a Long Short-Term Memory model in terms of several aggregated statistics and sequential characteristics. Further examination indicates that the proposed model learned the spatial structure and the temporal relationship of human mobility, which generally agrees with our empirical analysis. This observation suggests that the transformer framework can be a promising model for learning and understanding human movements.

Details

Language :
English
ISSN :
22209964
Volume :
13
Issue :
2
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.58e397280a244fc4b80e9e61fde13e5d
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi13020035