• Resumo

    Modelo de Predição de Conforto de Usuários do Transporte Coletivo

    Data de publicação: 29/04/2021

    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.

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