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Pregled bibliografske jedinice broj: 1212281

Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation

Vrbanić, Filip; Tišljarić, Leo; Majstorović, Željko; Ivanjko, Edouard
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation // 2022 30th Mediterranean Conference on Control and Automation (MED)
Atena, Grčka: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1093-1098 doi:10.1109/med54222.2022.9837279 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation

Autori
Vrbanić, Filip ; Tišljarić, Leo ; Majstorović, Željko ; Ivanjko, Edouard

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

Izvornik
2022 30th Mediterranean Conference on Control and Automation (MED) / - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 1093-1098

ISBN
978-166540673-4

Skup
30th Mediterranean Conference on Control and Automation (MED 2022)

Mjesto i datum
Atena, Grčka, 28.06.2022. - 01.07.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Connected and Autonomous Vehicles ; Mixed traffic flow ; Q-Learning ; Variable Speed Limit ; Urban Motorway ; Speed Transition Matrices

Sažetak
The ever-increasing growth of the car industry and the demand for personal vehicles have put current traffic management systems and infrastructure to strain. The enlarged number of vehicles in traffic flows often creates congestion due to the increased demand to use the existing road capacity. This is especially evident in urban areas that consist of urban roads and urban motorways. Increasing the capacity by building additional infrastructure is not always a feasible solution. Thus, approaches derived from Intelligent Transportation Systems are frequently used to increase the level of service, especially on urban motorways. The development of Connected and Autonomous Vehicles (CAVs) creates additional challenges and opportunities for the traffic management system to cope with. In this study, the Variable Speed Limit (VSL) based on Q-Learning (QL) with CAVs as actuators and mobile sensors combined with Speed Transition Matrices (STMs) for state estimation named STM-QL-VSL is developed and analyzed. Varying traffic scenarios with different CAV penetration rates are analyzed, including the comparison of motorway configuration with one and two applicable VSL zones. The developed STM-QL-VSL algorithm managed to learn the control policy for each tested scenario and improve measured macroscopic traffic parameters such as Total Time Spent and Mean Travel Time.

Izvorni jezik
Engleski

Znanstvena područja
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 Željko Majstorović (autor)

Avatar Url Edouard Ivanjko (autor)

Avatar Url Leo Tišljarić (autor)

Avatar Url Filip Vrbanić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Vrbanić, Filip; Tišljarić, Leo; Majstorović, Željko; Ivanjko, Edouard
Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation // 2022 30th Mediterranean Conference on Control and Automation (MED)
Atena, Grčka: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1093-1098 doi:10.1109/med54222.2022.9837279 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Vrbanić, F., Tišljarić, L., Majstorović, Ž. & Ivanjko, E. (2022) Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation. U: 2022 30th Mediterranean Conference on Control and Automation (MED) doi:10.1109/med54222.2022.9837279.
@article{article, author = {Vrbani\'{c}, Filip and Ti\v{s}ljari\'{c}, Leo and Majstorovi\'{c}, \v{Z}eljko and Ivanjko, Edouard}, year = {2022}, pages = {1093-1098}, DOI = {10.1109/med54222.2022.9837279}, keywords = {Connected and Autonomous Vehicles, Mixed traffic flow, Q-Learning, Variable Speed Limit, Urban Motorway, Speed Transition Matrices}, doi = {10.1109/med54222.2022.9837279}, isbn = {978-166540673-4}, title = {Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation}, keyword = {Connected and Autonomous Vehicles, Mixed traffic flow, Q-Learning, Variable Speed Limit, Urban Motorway, Speed Transition Matrices}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Atena, Gr\v{c}ka} }
@article{article, author = {Vrbani\'{c}, Filip and Ti\v{s}ljari\'{c}, Leo and Majstorovi\'{c}, \v{Z}eljko and Ivanjko, Edouard}, year = {2022}, pages = {1093-1098}, DOI = {10.1109/med54222.2022.9837279}, keywords = {Connected and Autonomous Vehicles, Mixed traffic flow, Q-Learning, Variable Speed Limit, Urban Motorway, Speed Transition Matrices}, doi = {10.1109/med54222.2022.9837279}, isbn = {978-166540673-4}, title = {Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation}, keyword = {Connected and Autonomous Vehicles, Mixed traffic flow, Q-Learning, Variable Speed Limit, Urban Motorway, Speed Transition Matrices}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Atena, Gr\v{c}ka} }

Časopis indeksira:


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

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