Creating representative urban motorway traffic scenarios: initial observations (CROSBI ID 707015)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Vrbanić, Filip ; Miletić, Mladen ; Ivanjko, Edouard ; Majstorović, Željko
engleski
Creating representative urban motorway traffic scenarios: initial observations
Traffic patterns are useful for analyzing and identifying representative traffic scenarios from traffic data. Traffic scenarios are important when machine learning is used for traffic control to ensure good controller performance in all cases. This article tackles the problem of identifying relevant scenarios from clustered data for urban mobility analysis. The unsupervised learning approaches k-means, principal component analysis, and self- organizing maps were applied on real traffic data from Slovenian motorways to analyze and group traffic scenarios. Obtained observations present a solid foundation for future research on a wide-scale data-set, including data from more measuring points for creating relevant traffic scenarios for learning of traffic controllers.
Intelligent Transportation Systems ; Urban Mobility ; Traffic Scenarios ; Data Science
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
183-188.
2021.
objavljeno
10.1109/ELMAR52657.2021.9550867
Podaci o matičnoj publikaciji
Proceedings of ELMAR-2021
Muštra, Mario ; Vuković, Josip ; Zovko-Cihlar, Branka
Zagreb: Hrvatsko društvo Elektronika u pomorstvu (ELMAR)
978-1-6654-4437-8
Podaci o skupu
63rd International Symposium ELMAR 2021
predavanje
12.09.2021-15.09.2021
Zadar, Hrvatska
Povezanost rada
Elektrotehnika, Računarstvo, Tehnologija prometa i transport