From mobility data to habits and common pathways

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Thiago Andrade Silva (Autor) Outra; Cancela, B (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Expert Systems

Vol. 37

ISSN: 0266-4720

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 9 Citações

Scopus - 9 Citações

Outras Informações

ID Authenticus: P-00S-NSK

DOI: 10.1111/exsy.12627

Abstract (EN): Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20