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

Autori
Van Hauwermeiren, Wout ; Filipan, Karlo ; Botteldooren, Dick ; De Coensel, Bert

Izvornik
Transportation Research Part D: Transport and Environment (1361-9209) 90 (2021); 102636, 17

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Road noise ; Rolling noise ; Blind sensor calibration ; Artificial neural networks ; De-noising autoencoder

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

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