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Author (up) Heredia, C.; Moreno, S.; Yushimito, W.F. doi  openurl
  Title Characterization of Mobility Patterns with a Hierarchical Clustering of Origin-Destination GPS Taxi Data Type
  Year 2022 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal IEEE Trans. Intell. Transp. Syst.  
  Volume 23 Issue 8 Pages 12700-12710  
  Keywords Machine learning; Taxi; GPS Data; Hierarchical Clustering; Urban Mobility Patterns  
  Abstract 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 thanks to its hierarchical structure.
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000732152100001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1462  
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