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Optimization of traffic lights operation using network load data. / Krylatov, Alexander; Puzach, Vladislav; Shatalova, Natalia; Asaul, Maksim.

In: Transportation Research Procedia, Vol. 50, 2020, p. 321-329.

Research output: Contribution to journalConference articlepeer-review

Harvard

Krylatov, A, Puzach, V, Shatalova, N & Asaul, M 2020, 'Optimization of traffic lights operation using network load data', Transportation Research Procedia, vol. 50, pp. 321-329. https://doi.org/10.1016/j.trpro.2020.10.038

APA

Krylatov, A., Puzach, V., Shatalova, N., & Asaul, M. (2020). Optimization of traffic lights operation using network load data. Transportation Research Procedia, 50, 321-329. https://doi.org/10.1016/j.trpro.2020.10.038

Vancouver

Krylatov A, Puzach V, Shatalova N, Asaul M. Optimization of traffic lights operation using network load data. Transportation Research Procedia. 2020;50:321-329. https://doi.org/10.1016/j.trpro.2020.10.038

Author

Krylatov, Alexander ; Puzach, Vladislav ; Shatalova, Natalia ; Asaul, Maksim. / Optimization of traffic lights operation using network load data. In: Transportation Research Procedia. 2020 ; Vol. 50. pp. 321-329.

BibTeX

@article{ee2a263029c742098e9bdf2dbf0fe50c,
title = "Optimization of traffic lights operation using network load data",
abstract = "Control of traffic lights in a road network of a large city is an extremely complicated task. However, the efficient use of network capacities is impossible without adjusting traffic lights operation cycles. To date, a lot of methods have been developed to adjust the operation of traffic lights based on classical transportation models. However, classical models of flow distribution are not sensitive to local effects taking place at intersections of a road network. This article shows that artificial neural networks and randomized algorithms of stochastic approximation allow building systems for traffic lights operation control that take into account various non-linear stochastic relations between locally observed network loads. The article describes a method to get such control and presents the results of testing the approach through the example of a test transportation network.",
keywords = "Direct neural network training, Randomized algorithms of stochastic approximation, Traffic lights control",
author = "Alexander Krylatov and Vladislav Puzach and Natalia Shatalova and Maksim Asaul",
note = "Funding Information: The work was jointly supported by a grant from the Russian Science oF undation (No. 19-71-10012. Multi-agent systems development for automatic remote control of traffic flows in congested urban road networks). Publisher Copyright: {\textcopyright} 2020 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIV International Conference 2020 SPbGASU “Organization and safety of traffic in large cities” Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 14th International Conference on Organization and Traffic Safety Management in Large Cities, OTS 2020 ; Conference date: 21-10-2020 Through 24-10-2020",
year = "2020",
doi = "10.1016/j.trpro.2020.10.038",
language = "English",
volume = "50",
pages = "321--329",
journal = "Transportation Research Procedia",
issn = "2352-1457",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Optimization of traffic lights operation using network load data

AU - Krylatov, Alexander

AU - Puzach, Vladislav

AU - Shatalova, Natalia

AU - Asaul, Maksim

N1 - Funding Information: The work was jointly supported by a grant from the Russian Science oF undation (No. 19-71-10012. Multi-agent systems development for automatic remote control of traffic flows in congested urban road networks). Publisher Copyright: © 2020 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIV International Conference 2020 SPbGASU “Organization and safety of traffic in large cities” Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - Control of traffic lights in a road network of a large city is an extremely complicated task. However, the efficient use of network capacities is impossible without adjusting traffic lights operation cycles. To date, a lot of methods have been developed to adjust the operation of traffic lights based on classical transportation models. However, classical models of flow distribution are not sensitive to local effects taking place at intersections of a road network. This article shows that artificial neural networks and randomized algorithms of stochastic approximation allow building systems for traffic lights operation control that take into account various non-linear stochastic relations between locally observed network loads. The article describes a method to get such control and presents the results of testing the approach through the example of a test transportation network.

AB - Control of traffic lights in a road network of a large city is an extremely complicated task. However, the efficient use of network capacities is impossible without adjusting traffic lights operation cycles. To date, a lot of methods have been developed to adjust the operation of traffic lights based on classical transportation models. However, classical models of flow distribution are not sensitive to local effects taking place at intersections of a road network. This article shows that artificial neural networks and randomized algorithms of stochastic approximation allow building systems for traffic lights operation control that take into account various non-linear stochastic relations between locally observed network loads. The article describes a method to get such control and presents the results of testing the approach through the example of a test transportation network.

KW - Direct neural network training

KW - Randomized algorithms of stochastic approximation

KW - Traffic lights control

UR - http://www.scopus.com/inward/record.url?scp=85096974243&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e4e04945-2aa9-3257-b784-475a3ef5fedd/

U2 - 10.1016/j.trpro.2020.10.038

DO - 10.1016/j.trpro.2020.10.038

M3 - Conference article

AN - SCOPUS:85096974243

VL - 50

SP - 321

EP - 329

JO - Transportation Research Procedia

JF - Transportation Research Procedia

SN - 2352-1457

T2 - 14th International Conference on Organization and Traffic Safety Management in Large Cities, OTS 2020

Y2 - 21 October 2020 through 24 October 2020

ER -

ID: 71562890