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  • Opportunistic monitoring of pavements for noise labeling and mitigation with machine learning

    Author(s)
    Van Hauwermeiren, W
    Filipan, K
    Botteldooren, D
    De Coensel, B
    Griffith University Author(s)
    De Coensel, Bert
    Year published
    2021
    Metadata
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    Abstract
    Currently, municipalities assess rolling noise on road surfaces using Close-Proximity measurements (CPX). To avoid these labor-intensive measurements, an opportunistic approach based on commodity sensors in a fleet of cars, is proposed. Blind sensor calibration eliminates the effect of measurement vehicle and varying observation conditions. Calibration relies on spatial coherence: modifiers and confounders do not interact strongly with location while the quantity of interest depends on location and less on measurement vehicle. Generalized additive speed models, car offset and de-noising autoencoders (DAE) were investigated. ...
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    Currently, municipalities assess rolling noise on road surfaces using Close-Proximity measurements (CPX). To avoid these labor-intensive measurements, an opportunistic approach based on commodity sensors in a fleet of cars, is proposed. Blind sensor calibration eliminates the effect of measurement vehicle and varying observation conditions. Calibration relies on spatial coherence: modifiers and confounders do not interact strongly with location while the quantity of interest depends on location and less on measurement vehicle. Generalized additive speed models, car offset and de-noising autoencoders (DAE) were investigated. DAE achieves prominent results: (1) ratio of variability of measurements at a single location to the variability of measurements over all locations increases, (2) convergence of mean measurement at a location is faster, and (3) seasonal effects are eliminated. Finally, although the proposed method includes a diversity of tires, below 1600 Hz its results differ from CPX less than the difference between bi-annually repeated CPX measurements.
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    Journal Title
    Transportation Research Part D: Transport and Environment
    Volume
    90
    DOI
    https://doi.org/10.1016/j.trd.2020.102636
    Subject
    Environmental Science and Management
    Urban and Regional Planning
    Transportation and Freight Services
    Publication URI
    http://hdl.handle.net/10072/401079
    Collection
    • Journal articles

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