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Title: A study on the behavior of clustering techniques for modeling travel time in road-based mass transit systems
Authors: Cristóbal, Teresa
Padrón Morales, Gabino 
Quesada Arencibia, Francisco Alexis 
Alayón Hernández, Francisco Javier 
De Blasio , Gabriele Salvatore 
García Rodríguez, Carmelo Rubén 
UNESCO Clasification: 3327 Tecnología de los sistemas de transporte
120304 Inteligencia artificial
Keywords: Clustering
Data mining
Intelligent transport systems
Mass transit systems
Issue Date: 2019
Journal: Proceedings (MDPI) 
Conference: 13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence - UCAmI 2019 
Abstract: In road-based mass transit systems, the travel time is a key factor affecting quality of service. For this reason, to know the behavior of this time is a relevant challenge. Clustering methods are interesting tools for knowledge modeling because these are unsupervised techniques, allowing hidden behavior patterns in large data sets to be found. In this contribution, a study on the utility of different clustering techniques to obtain behavior pattern of travel time is presented. The study analyzed three clustering techniques: K-medoid, Diana, and Hclust, studying how two key factors of these techniques (distance metric and clusters number) affect the results obtained. The study was conducted using transport activity data provided by a public transport operator.
ISSN: 2504-3900
DOI: 10.3390/proceedings2019031018
Source: Proceedings (MDPI) [ISSN 2504-3900], v. 31 (1), 18
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