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

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

Blog Article

In SLEEP TIGHT MELATONIN 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 Quick Key Set 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.