Pregled bibliografske jedinice broj: 1019646
Opportunistic In-Vehicle Noise Measurements assess Road Surface Quality to Improve Noise Mapping: Preliminary Results from the MobiSense Project
Opportunistic In-Vehicle Noise Measurements assess Road Surface Quality to Improve Noise Mapping: Preliminary Results from the MobiSense Project // Proceedings of the 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 / Ochmann, Martin (ur.).
Aachen, 2019. str. 7987-7994 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1019646 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Opportunistic In-Vehicle Noise Measurements assess Road Surface Quality to Improve Noise Mapping: Preliminary Results from the MobiSense Project
Autori
Dekoninck, Luc ; Van Hauwermeiren, Wout ; David, Joachim ; Filipan, Karlo ; De Pessemier, Toon ; De Coensel, Bert ; Joseph, Wout ; Martens, Luc ; Botteldooren, Dick
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019
/ Ochmann, Martin - Aachen, 2019, 7987-7994
Skup
23rd International Congress on Acoustics ; 4th EAA Euroregio 2019
Mjesto i datum
Aachen, Njemačka, 09.09.2019. - 13.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Noise mapping, Road surface, Big Data
Sažetak
The quality of road pavements affects noise emission caused by tire-road interactions. This in turn affects the health and well-being of residents near these roads. Road pavement quality degrades over time due to wear, accidents, and infrastructure works. These local features are usually not included in noise mapping due to the lack of high-quality information on pavements with enough spatial resolution.The aim of Mobisense is to assess the quality of the road surface by performing opportunistic noise and vibration measurements inside vehicles that are on the road for other purposes than road quality measurement. In the demonstrator phase of the project, 20 vehicles collect data while the drivers make their usual trips. Measurements from all vehicles are combined using machine learning techniques. This removes engine noise, corrects for vehicle specific speed dependence, and finally determines a rolling noise proxy in third-octave bands. This rolling noise correction includes the effect of pavement type as well as the effect of road surface degradation. This local variation in road surface quality is included as a correction in the rolling noise component of CNOSSOS and used to calculate a subset of the noise map for the Flemish region in Belgium. Including road surface quality in this way changes noise maps locally over a range of 6 dBA.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
