Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/349
Title: Real-time traffic signal control with swarm optimization methods
Authors: Çeltek, Seyit Alperen
Durdu, Akif
Ali, Muzamil Eltejani Mohammed
Keywords: Traffic Signal Control
Real-Time Control
Heuristic Control
Particle Swarm Optimization
Particle Swarm
Algorithm
Issue Date: 2020
Publisher: ELSEVIER SCI LTD
Abstract: Real-time traffic signal control is the control methods that control the traffic signal according to the instant traffic situation. In this paper, it is suggested to optimize the traffic control problem with the swarm-based heuristic optimization algorithms. The proposed methods are tested with the real traffic data obtained from Kilis city in Turkey. The performance is evaluated in real-time via the SUMO traffic simulator. The obtained results are compared with the actual traffic measurement data, and the success of the proposed method is expressed numerically. Finally, it is proved that the particle swarm optimization algorithm and its variance algorithm could be used successfully to optimize the traffic signals control in real traffic. In this study, Social Learning-Particle Swarm Optimization is used as a traffic signal optimizer for the first time in the known literature. (C) 2020 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.measurement.2020.108206
https://hdl.handle.net/20.500.13091/349
ISSN: 0263-2241
1873-412X
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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