Characterization of Mobility Patterns With a Hierarchical Clustering of Origin-Destination GPS Taxi Data

Cristobal Heredia, Sebastian Moreno, Wilfredo F. Yushimito

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)

Resumen

Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure.

Idioma originalInglés
Páginas (desde-hasta)12700-12710
Número de páginas11
PublicaciónIEEE Transactions on Intelligent Transportation Systems
Volumen23
N.º8
DOI
EstadoPublicada - 1 ago. 2022
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Characterization of Mobility Patterns With a Hierarchical Clustering of Origin-Destination GPS Taxi Data'. En conjunto forman una huella única.

Citar esto