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master's thesis
Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova
Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences, 2021. urn:nbn:hr:119:997364
Jakobović, Žana
University of Zagreb
Faculty of Transport and Traffic Sciences
Division of Intelligent Transport Systems and Logistics
Department of Intelligent Transport Systems

Institutional repository: Faculty of Transport and Traffic Sciences

Cite this document

Jakobović, Ž. (2021). Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova (Master's thesis). Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences. Retrieved from https://urn.nsk.hr/urn:nbn:hr:119:997364

Jakobović, Žana. "Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova." Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, 2021. https://urn.nsk.hr/urn:nbn:hr:119:997364

Jakobović, Žana. "Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova." Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, 2021. https://urn.nsk.hr/urn:nbn:hr:119:997364

Jakobović, Ž. (2021). 'Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova', Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:119:997364

Jakobović Ž. Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova [Master's thesis]. Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences; 2021 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:119:997364

Ž. Jakobović, "Primjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova", Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:119:997364

Metadata
TitlePrimjena strojnog učenja za upravljanje raskrižjima u okruženju mješovitih prometnih tokova
Title (english)Application of Machine Learning for Intersection Control in Mixed Flow Environment
AuthorŽana Jakobović
MentorEdouard Ivanjko (mentor)
MentorDino Čakija (komentor)
Committee memberMiroslav Vujić (predsjednik povjerenstva)
Committee memberMartin Gregurić (član povjerenstva)
Committee memberSadko Mandžuka (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Transport and Traffic Sciences
(Division of Intelligent Transport Systems and Logistics)
(Department of Intelligent Transport Systems)
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Intelligent Transport Systems and Logistics
Abstract
Porast broja stanovništva uzrokovao je povećanje prijevozne potražnje što dovodi do svakodnevnih prometnih zagušenja, posebno u vršnim satima. Problem nastaje kada prijevozna potražnja postane veća od prijevozne ponude te prometnice nemaju dovoljan kapacitet za visoku razinu usluge. U gradskoj sredini zagušenja su izražena oko semaforiziranih raskrižja jer dolazi do preplitanja dvaju ili više prometnih tokova. Kako bi se smanjilo vrijeme putovanja i duljine repova čekanja moguće je koristiti metode strojnog učenja za prilagodbu signalnih planova. Pri tome mješoviti prometni tokovi koji sadrže ljudski upravljana vozila i umrežena autonomna vozila predstavljaju novi nadolazeći upravljački problem. Problemi nastaju zbog različitog percipiranja okoline i poštivanja pravila ponašanja kod vozača u ljudski upravljanim vozilima te autonomnim i umreženim autonomnim vozilima. U radu su opisane značajke mješovitih prometnih tokova, upravljački koncepti za slučaj semaforiziranih raskrižja i mješovitih tokova te analizirane mogućnosti primjene metoda strojnog učenja za upravljanje mješovitim prometnim tokovima.
Abstract (english)
The increase of the population caused the increase in transport demand which causes daily traffic congestions, especially during peak hours. The problem arises when traffic demand exceeds the traffic offer, and roads do not have enough capacity for a high level of service. In the urban environment, congestions are especially expressed around signalized intersections because two or more traffic flow intertwine. To decrease the travel time and waiting queue length, it is possible to implement machine learning methods to adapt intersection signal plans. Thereby, mixed traffic flows consisting of human-driven vehicles and connected autonomous vehicles represent an upcoming control issue. Problems arise due to different environment perceptions and respecting the rules with human-driven vehicles and autonomous and connected autonomous vehicles. In this thesis, mixed traffic flow features, traffic management concepts for signalized intersections, mixed traffic flows are described, and the possibilities of implementing machine learning methods for managing mixed traffic flows are analyzed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:119:997364
Study programmeTitle: Intelligent Transport Systems and Logistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka prometa (magistar/magistra inženjer/inženjerka prometa)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-07-09 14:21:36