Pregled bibliografske jedinice broj: 1005432
Classification of Travel Modes Using Streaming GNSS Data
Classification of Travel Modes Using Streaming GNSS Data // TRANSCOM 2019 13th International Scientific Conference on Sustainable, Modern and Safe Transport / Bujňák, Ján ; Guagliano, Mario (ur.).
Vysoké Tatry, Slovačka: Elsevier, 2019. str. 209-216 doi:10.1016/j.trpro.2019.07.032 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Classification of Travel Modes Using Streaming GNSS Data
Autori
Erdelić, Martina ; Carić, Tonči ; Ivanjko, Edouard ; Jelušić, Niko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
TRANSCOM 2019 13th International Scientific Conference on Sustainable, Modern and Safe Transport
/ Bujňák, Ján ; Guagliano, Mario - : Elsevier, 2019, 209-216
Skup
13th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM)
Mjesto i datum
Vysoké Tatry, Slovačka, 29.05.2019. - 31.05.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
GNSS data ; data stream ; travel mode ; classification ; Random Forest ; k Nearest Neighbors
Sažetak
Over the last decade, smartphones became a valuable source of traffic data. GNSS data and other data from smartphone sensors can be successfully used in travel mode classification. Travel mode classification data are a significant source of information for various applications such as travel planning, urban road operations or user behavior understanding. Today, the availability of access to real-time data streams makes fast and real-time classification of travel modes possible. Because of different characteristics of data streams, the applied classification method has to be adjusted to the particular data stream. In this paper two classification methods, k Nearest Neighbors and Random Forest, are compared with emphasis on accuracy. First, they are applied for classification of travel modes using a static GNSS dataset, and afterward using streaming GNSS data. For the purpose of classification, characteristic distribution of velocity and acceleration for different travel modes is determined. Regarding streaming GNSS data, the influence of the window size on the classification accuracy is analyzed. Obtained results show that both classification methods can be successfully applied for the classification of travel modes.
Izvorni jezik
Engleski
Znanstvena područja
Tehnologija prometa i transport
Napomena
Transportation Research Procedia
Volume 40, 2019, Pages 209-216
POVEZANOST RADA
Ustanove:
Fakultet prometnih znanosti, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Conference Proceedings Citation Index - Science (CPCI-S)
- Conference Proceedings Citation Index - Social Sciences & Humanities (CPCI-SSH)
- Scopus