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Pregled bibliografske jedinice broj: 1079597

Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation


Gregurić, Martin; Kušić, Krešimir; Vrbanić, Filip; Ivanjko, Edouard
Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation // Proceedings OF ELMAR-2020 / Muštra, Mario ; Vuković, Vuković ; Zovko-Cihlar, Branka (ur.).
Zagreb, 2020. str. 67-72 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1079597 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation

Autori
Gregurić, Martin ; Kušić, Krešimir ; Vrbanić, Filip ; Ivanjko, Edouard

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

Izvornik
Proceedings OF ELMAR-2020 / Muštra, Mario ; Vuković, Vuković ; Zovko-Cihlar, Branka - Zagreb, 2020, 67-72

ISBN
978-1-7281-5972-0

Skup
62nd International Symposium ELMAR-2020

Mjesto i datum
Zadar, Hrvatska, 14-15.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Traffic Control ; Variable Speed Limit Control ; Intelligent Transportation Systems ; Learning Systems ; Deep Q-Learning Network ; Criteria Functions ; Performance Analysis

Sažetak
Today’s urban motorways cannot fulfill their pur-pose to simultaneously serve transit and local urban trafficwith a high Level of Service. In the case of urban motorwayinfrastructure, the traditional "build only" approach is notalways possible due to the lack of space. This study is focused onthe Variable Speed Limit Control (VSLC) as one of the trafficcontrol methods applicable for any type of motorway and Q-learning as one commonly used approach for designing learningbased VSLC algorithms. The drawback of this methodology isthe representation and exploration of the large state-action spaceas it is the case in its application for VSLC. This study introducesa Deep Q-Network to approximate the Q-function and presents anovel learning approach for the VSLC application with possibilityto track vehicles on the microscopic level. The proposed rewardfunction steers the learning towards the improvement of rewardand prevention of oscillation among consecutive speed limits.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport



POVEZANOST RADA


Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )

Ustanove:
Fakultet prometnih znanosti, Zagreb

Citiraj ovu publikaciju

Gregurić, Martin; Kušić, Krešimir; Vrbanić, Filip; Ivanjko, Edouard
Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation // Proceedings OF ELMAR-2020 / Muštra, Mario ; Vuković, Vuković ; Zovko-Cihlar, Branka (ur.).
Zagreb, 2020. str. 67-72 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Gregurić, M., Kušić, K., Vrbanić, F. & Ivanjko, E. (2020) Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation. U: Muštra, M., Vuković, V. & Zovko-Cihlar, B. (ur.)Proceedings OF ELMAR-2020.
@article{article, year = {2020}, pages = {67-72}, keywords = {Traffic Control, Variable Speed Limit Control, Intelligent Transportation Systems, Learning Systems, Deep Q-Learning Network, Criteria Functions, Performance Analysis}, isbn = {978-1-7281-5972-0}, title = {Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation}, keyword = {Traffic Control, Variable Speed Limit Control, Intelligent Transportation Systems, Learning Systems, Deep Q-Learning Network, Criteria Functions, Performance Analysis}, publisherplace = {Zadar, Hrvatska} }
@article{article, year = {2020}, pages = {67-72}, keywords = {Traffic Control, Variable Speed Limit Control, Intelligent Transportation Systems, Learning Systems, Deep Q-Learning Network, Criteria Functions, Performance Analysis}, isbn = {978-1-7281-5972-0}, title = {Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation}, keyword = {Traffic Control, Variable Speed Limit Control, Intelligent Transportation Systems, Learning Systems, Deep Q-Learning Network, Criteria Functions, Performance Analysis}, publisherplace = {Zadar, Hrvatska} }




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