期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 8, 页码 12700-12710出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3116963
关键词
Public transportation; Global Positioning System; Clustering methods; Clustering algorithms; Data models; Urban areas; Data mining; Machine learning; taxi; GPS data; hierarchical clustering; urban mobility patterns
资金
- Programa de Formacion de estudiantes en investigacion FEI-2020
- Programa de Apoyo a la Investigacion 2020, DII/VRA of the Universidad Adolfo Ibanez
This paper introduces a new taxi data clustering model called OD-means, which is a hierarchical adaptive k-means algorithm based on origin-destination pairs, successfully tested on taxi GPS data from Santiago, Chile.
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.
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