Rolling vibes: continuous transport infrastructure monitoring

Fatjon Seraj

    Research output: ThesisPhD Thesis - Research UT, graduation UT

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    Transport infrastructure is a people to people technology, in the sense that is build by people to serve people, by facilitating transportation, connection and communication. People improved infrastructure by applying simple methods derived from their sensing and thinking. Since the early ages, humans knew that infrastructure should avoid certain amount of vibration that affected either the passenger or the carriage. They achieved that knowledge using the human body sensing capabilities. In fact the human body is the perfect complex sensor node, sensing a wide range of bandwidths, starting from vibrations to the radio waves.
    Nowadays, specific systems are available that can determine the quality of the road surface by means of special measurements in a somewhat more systematic way. However, a new paradigm of crowd based sensing is emerging, as a result of the smart mobile device revolution. These devices are becoming as ubiquitous as the people that carry them. As daily commuters travel around, their senses are trained to detect road pavement irregularities, uncomfortable turns, potholes, road joints, rail road bumps, stations, accelerations, deceleration and so on. If a human can detect these situations just by classifying the vibration level, why cannot the smartphones in their pocket do the same thing? And same as people do, what a smartphone skipped or missed learning can be compensated by more and more knowledge provided by an army of smartphones participating in infrastructure monitoring. This naive reasoning gave rise to further reasoning and studying the nature of vibration resulting in four long years of research and this thesis. Smartphone based crowd-sensing is not a new concept, there have been previous attempts to make use of this all-around technology. However, a major challenge is the fact that the smartphones are very diverse, have no accurate sensors, and are non-deterministic by nature. On the other hand being ubiquitous, they provide the ability to continuously measure because of the available processor, memory, and (wireless) communication tools.
    Correct handling of these inaccuracies and unreliable data has been a central theme of the research. Like people, intelligence, learning systems, and additional observations are used to compensate for inaccurate and incomplete observations. The signals measured from the transport infrastructure are a function of four parameters time, distance, temporal frequency and spatial frequency, each with a limited degree of accuracy. In this thesis, we show that with the help of advanced signal processing and machine learning, despite the many inaccuracies in the observations, we can accurately reflect road quality and type of damage. The information obtained by the smartphone sensors is first processed locally by wavelet decomposition methods and useful features are calculated which are then clustered. To compensate for the position inaccuracies, a new aggregation and visualization algorithm has been developed. In addition to a more or less direct measurement of the vibrations, an indirect method is also used, taking into account the driver’s driving behavior.
    The algorithms, methods, and techniques have been extensively tested and evaluated in various scenarios (for motor vehicles, cyclists, and wheelchairs). Certain indicators are computed to reflect the state-of-the-art requirements. In addition to measuring the quality of the road surface, the quality of railroad track geometry has also been measured.
    Original languageEnglish
    Awarding Institution
    • University of Twente
    • Havinga, P.J.M., Supervisor
    • Tinga, Tiedo, Supervisor
    • Meratnia, N., Supervisor
    Award date28 Jun 2017
    Place of PublicationEnschede
    Print ISBNs978-90-365-4363-7
    Publication statusPublished - 28 Jun 2017


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