Knowledge discovery applied in modal rail

This paper presents a methodology to obtain rules of conduction from a set of data captured from sensors placed at a train as well data of actions executed by drivers. These actions result in a history H. The knowledge discovered is put in practice in a driving simulator and the result of the simulated actions generates a history H´. The validation of the discovered knowledge is done in an objective manner, which is calculated as a degree of similarity between the records. This degree of similarity reflects the performance of knowledge discovery process, which in experiments was around 85%. This degree of similarity represents how next were H and H.