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Pregled bibliografske jedinice broj: 1112538

In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB

Pavleski, Daniel; Miletić, Mladen; Nečoska, Daniela Koltovska; Ivanjko, Edouard
In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB // Transformation of Transportation / Petrović, Marjana ; Novačko, Luka (ur.).
Cham: Springer, 2021. str. 49-64 doi:10.1007/978-3-030-66464-0_4 (predavanje, recenziran, cjeloviti rad (in extenso), znanstveni)

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Naslov
In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB

Autori
Pavleski, Daniel ; Miletić, Mladen ; Nečoska, Daniela Koltovska ; Ivanjko, Edouard

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

Izvornik
Transformation of Transportation / Petrović, Marjana ; Novačko, Luka - Cham : Springer, 2021, 49-64

ISBN
978-3-030-66463-3

Skup
International Scientific Conference "The Science and Development of Transport" (ZIRP 2020): Transformation of Transportation

Mjesto i datum
Online, 29.09.2020. - 30.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Recenziran

Ključne riječi
Intelligent Transportation Systems ; Adaptive Traffic Signal Control ; Reinforcement Learning ; Fixed Time Signal Control ; Evaluation ; Microscopic Traffic Simulation

Sažetak
Adaptive Traffic Signal Control (ATSC) is today widely applied for managing traffic on signalized intersections due to its capability to reduce congestion. ATSC changes the signal programs in real-time according to the measured current incoming traffic flows when ATSC is applied. This results in an improvement in the throughput of urban networks. However, prior to the implementation of such systems they be evaluated. Evaluation of the effectiveness of complex ATSC is still a challenge and presents an open problem. For the evaluation, different measures of effectiveness to gather in-depth insight into the traffic situations of the controlled signalized intersection are needed. In this paper, an augmented version of the previously developed MATLAB based tool TSCLab (Traffic Signal Control Laboratory) is applied to evaluate a newly proposed ATSC based on self-organizing maps and reinforcement learning. The performance of the mentioned ATSC is evaluated using appropriately chosen measures of effectiveness obtained in real-time using a microscopic simulation environment based on VISSIM and a realistic traffic scenario. Obtained simulation results reveal that ATSC uses shorter phase and cycle duration, achieving a lower green time utilization but also shorter queue lengths, thus improving the throughput of the analyzed intersection compared to the existing fixed- time signal control.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport



POVEZANOST RADA

Projekti:
--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 Mladen Miletić (autor)

Avatar Url Edouard Ivanjko (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Pavleski, Daniel; Miletić, Mladen; Nečoska, Daniela Koltovska; Ivanjko, Edouard
In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB // Transformation of Transportation / Petrović, Marjana ; Novačko, Luka (ur.).
Cham: Springer, 2021. str. 49-64 doi:10.1007/978-3-030-66464-0_4 (predavanje, recenziran, cjeloviti rad (in extenso), znanstveni)
Pavleski, D., Miletić, M., Nečoska, D. & Ivanjko, E. (2021) In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB. U: Petrović, M. & Novačko, L. (ur.)Transformation of Transportation doi:10.1007/978-3-030-66464-0_4.
@article{article, author = {Pavleski, Daniel and Mileti\'{c}, Mladen and Ne\v{c}oska, Daniela Koltovska and Ivanjko, Edouard}, year = {2021}, pages = {49-64}, DOI = {10.1007/978-3-030-66464-0\_4}, keywords = {Intelligent Transportation Systems, Adaptive Traffic Signal Control, Reinforcement Learning, Fixed Time Signal Control, Evaluation, Microscopic Traffic Simulation}, doi = {10.1007/978-3-030-66464-0\_4}, isbn = {978-3-030-66463-3}, title = {In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB}, keyword = {Intelligent Transportation Systems, Adaptive Traffic Signal Control, Reinforcement Learning, Fixed Time Signal Control, Evaluation, Microscopic Traffic Simulation}, publisher = {Springer}, publisherplace = {online} }
@article{article, author = {Pavleski, Daniel and Mileti\'{c}, Mladen and Ne\v{c}oska, Daniela Koltovska and Ivanjko, Edouard}, year = {2021}, pages = {49-64}, DOI = {10.1007/978-3-030-66464-0\_4}, keywords = {Intelligent Transportation Systems, Adaptive Traffic Signal Control, Reinforcement Learning, Fixed Time Signal Control, Evaluation, Microscopic Traffic Simulation}, doi = {10.1007/978-3-030-66464-0\_4}, isbn = {978-3-030-66463-3}, title = {In-Depth Evaluation of Reinforcement Learning Based Adaptive Traffic Signal Control Using TSCLAB}, keyword = {Intelligent Transportation Systems, Adaptive Traffic Signal Control, Reinforcement Learning, Fixed Time Signal Control, Evaluation, Microscopic Traffic Simulation}, publisher = {Springer}, publisherplace = {online} }

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