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Pregled bibliografske jedinice broj: 1139677

Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows

Vrbanić, Filip; Ivanjko, Edouard; Mandžuka, Sadko; Miletić, Mladen
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows // Proceedings of 2021 29th Mediterranean Conference on Control and Automation (MED)
Apulija, Italija: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 560-565 doi:10.1109/MED51440.2021.9480215 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows

Autori
Vrbanić, Filip ; Ivanjko, Edouard ; Mandžuka, Sadko ; Miletić, Mladen

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 2021 29th Mediterranean Conference on Control and Automation (MED) / - : Institute of Electrical and Electronics Engineers (IEEE), 2021, 560-565

ISBN
978-1-6654-2258-1

Skup
29th Mediterranean Conference on Control and Automation (MED 2021)

Mjesto i datum
Apulija, Italija, 22.06.2021. - 25.06.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Intelligent Transportation Systems, Mixed Traffic Flows, Variable Speed Limit Control, Artificial Intelligence, Urban motorways

Sažetak
Today’s urban mobility requires results for resolving increasingly complex demands on the traffic management system. Hence, the main problem is to achieve a satisfactory level of service for urban motorways as part of the urban traffic network. In addition, with the introduction of Connected and Autonomous Vehicles (CAVs), additional challenges for modern control systems arise. This study focuses on the Variable Speed Limit (VSL) based on Q-Learning with CAVs as actuators in the control loop. The Q-Learning algorithm is combined with the two-step Temporal Difference target to increase the effectiveness of the algorithm for learning the VSL control policy for mixed traffic flows. Different CAV penetration rates are analyzed, and the results are compared with a rule-based VSL and the no control case. The obtained results show that Q-Learning based VSL can learn the control policy and improve the Total Travel Time and Mean Travel Time for different CAV penetration rates. The results are most apparent in the case of low CAV penetration rates. There is also an indication that the increase of the CAV penetration rate reduces the need for separate VSL control.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport



POVEZANOST RADA

Projekti:
HRZZ-IP-2020-02-5042 - Razvoj sustava zasnovanih na učećim agentima za unaprijeđenje upravljanja prometom u gradovima (DLASIUT) (Ivanjko, Edouard, HRZZ - 2020-02) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)

Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Sadko Mandžuka (autor)

Avatar Url Edouard Ivanjko (autor)

Avatar Url Filip Vrbanić (autor)

Avatar Url Mladen Miletić (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Vrbanić, Filip; Ivanjko, Edouard; Mandžuka, Sadko; Miletić, Mladen
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows // Proceedings of 2021 29th Mediterranean Conference on Control and Automation (MED)
Apulija, Italija: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 560-565 doi:10.1109/MED51440.2021.9480215 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Vrbanić, F., Ivanjko, E., Mandžuka, S. & Miletić, M. (2021) Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows. U: Proceedings of 2021 29th Mediterranean Conference on Control and Automation (MED) doi:10.1109/MED51440.2021.9480215.
@article{article, author = {Vrbani\'{c}, Filip and Ivanjko, Edouard and Mand\v{z}uka, Sadko and Mileti\'{c}, Mladen}, year = {2021}, pages = {560-565}, DOI = {10.1109/MED51440.2021.9480215}, keywords = {Intelligent Transportation Systems, Mixed Traffic Flows, Variable Speed Limit Control, Artificial Intelligence, Urban motorways}, doi = {10.1109/MED51440.2021.9480215}, isbn = {978-1-6654-2258-1}, title = {Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows}, keyword = {Intelligent Transportation Systems, Mixed Traffic Flows, Variable Speed Limit Control, Artificial Intelligence, Urban motorways}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Apulija, Italija} }
@article{article, author = {Vrbani\'{c}, Filip and Ivanjko, Edouard and Mand\v{z}uka, Sadko and Mileti\'{c}, Mladen}, year = {2021}, pages = {560-565}, DOI = {10.1109/MED51440.2021.9480215}, keywords = {Intelligent Transportation Systems, Mixed Traffic Flows, Variable Speed Limit Control, Artificial Intelligence, Urban motorways}, doi = {10.1109/MED51440.2021.9480215}, isbn = {978-1-6654-2258-1}, title = {Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows}, keyword = {Intelligent Transportation Systems, Mixed Traffic Flows, Variable Speed Limit Control, Artificial Intelligence, Urban motorways}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Apulija, Italija} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Conference Proceedings Citation Index - Science (CPCI-S)
  • Scopus

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