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.

The main aim of the project, presented in this paper, is to create a system that will give a solution to the variety of situations we can encounter in industrial plants control; to achieve this goal we have given the system different automation, remote control and remote monitoring functionalities. Industrial installations can be composed of several plants; for this reason we offer a solution capable of defining a central office that is in control of all plants, each plant with its own controller. Apart from the problem of geographically disperse locations, it is also of the essence to offer automation for the tasks that take place in the plants. In order to achieve an automation system capable of giving answer to a range of situations, as wide as possible, we have designed an engine so that the user may create its own rule based system, defining himself the rules by means of an interface that abstracts the user from the formal definition of the rules.