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The small and medium-size cities are facing problems related to mobility that could be avoided by adopting the public transportation system, as buses and trains. However, in many Brazilian cities the use of public transportation is neglected because it is considered uncomfortable, expensive and insecure. To attract passengers for such kind of transportation there are several possible approaches, the promotion of comfort perception is one of those. Several studies have already approached this problem, however, few of them addressed the perception of comfort felt by the passengers using telemetry data collected from the vehicle. Among the works that use such data, none of them applied data mining techniques to abstract a general model of comfort perception. Therefore, this work aims to apply mining techniques over telemetry data collected from vehicles to build a comprehensible model to classify the level of comfort of public transportation passengers. To achieve this objective machine learning techniques were used, centered on decision trees. Due to the complexity of abstracting the model there were constructed three models, one for each acceleration axis that were merged using a meta-classifier responsible to point out the passenger general comfort. The results have reached an accuracy of 85,2%, which can be considered a promising result regarding the difficulties of separating the data source in sets that can better identify the bus drivers behaviour.

Modelo de Predição de Conforto de Usuários do Transporte Coletivo