Research output: Contribution to journal › Conference article › peer-review
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 journal › Conference article › peer-review
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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