A novel adaptive traffic signal control based on cloud/fog/edge computing
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper proposes the Internet of Things-based real-time adaptive traffic signal control strategy. The proposed model consists of three-layer; edge computing layer, fog computing layer, and cloud computing layer. The edge computing layer provides real-time and local optimization. The middle layer, which is the fog computing layer, performs a real-time and global optimization process. The cloud computing layer, which is the top layer, acts as a control center and optimizes the parameters of the fog layer and the edge layer. The proposed strategy uses the Deep Q-Learning algorithm for the optimization process in all three layers. This study employs the SUMO traffic simulator for performance evaluation. These results are compared with the results of adaptive traffic control methods. The output of this study shows that the proposed model can reduce waiting times and travel times while increasing travel speed.
Açıklama
WOS:000834692500001
Anahtar Kelimeler
Adaptive Traffic Signal Control, Internet Of Things, Reinforcement Learning
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Çeltek, S. A., Durdu, A. (2022). A novel adaptive traffic signal control based on cloud/fog/edge computing. International Journal Of Intelligent Transportation Systems Research.
