Pregled bibliografske jedinice broj: 1226894
Traffic Emissions Clustering Using OBD-II Dataset Based on Machine Learning Algorithms
Traffic Emissions Clustering Using OBD-II Dataset Based on Machine Learning Algorithms // International Scientific Conference “The Science and Development of Transport - Znanost i razvitak prometa” / Petrovic, Marjana ; Dovbischuk, Irina ; Cunha, André Luiz (ur.).
Šibenik, Hrvatska: Elsevier, 2022. str. 364-371 doi:10.1016/j.trpro.2022.09.040 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Traffic Emissions Clustering Using OBD-II Dataset
Based on Machine Learning Algorithms
Autori
Vaiti, Tin ; Tišljarić, Leo ; Erdelić, Tomislav ; Carić, Tonči
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
International Scientific Conference “The Science and Development of Transport - Znanost i razvitak prometa”
/ Petrovic, Marjana ; Dovbischuk, Irina ; Cunha, André Luiz - : Elsevier, 2022, 364-371
Skup
International Scientific Conference “The Science and Development of Transport - Znanost i razvitak prometa” (ZIRP 2022)
Mjesto i datum
Šibenik, Hrvatska, 28.09.2022. - 30.09.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
emissions prediction ; machine learning ; emissions clustering ; OBD data ; road traffic
Sažetak
Traffic emissions are one of the main causes of air pollution in developed urban areas. Estimating emissions patterns presents an ongoing challenge for the research and decision-making communities to detect and propose solutions for system stakeholders contributing to the pollution the most. This paper proposes a data-driven methodology for estimating the emissions clusters based on vehicle parameters that correlates to emissions. Research is based on a publicly available dataset with two main steps that include cluster number identification and the method for estimating the best-performing clustering algorithm for a given dataset. The research resulted in five emission classes based on the extracted features related to vehicle parameters and fuel consumption. The emissions clusters can be used for estimating the environmental impact of different vehicles in urban areas and producing air pollution mappings based on vehicle types.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Tehnologija prometa i transport
POVEZANOST RADA
Ustanove:
Fakultet prometnih znanosti, Zagreb
Poveznice na cjeloviti tekst rada:
Pristup cjelovitom tekstu rada doi www.sciencedirect.com www.researchgate.netCitiraj ovu publikaciju:
Časopis indeksira:
- Scopus