A Study on the Behavior of Clustering Techniques for Modeling Travel Time in Road-Based Mass Transit Systems

oleh: Teresa Cristóbal, Gabino Padrón, Alexis Quesada-Arencibia, Francisco Alayón, Gabriel de Blasio, Carmelo R. García

Format: Article
Diterbitkan: MDPI AG 2019-11-01

Deskripsi

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.