Open Access
IEEE Access, volume 11, pages 58516-58531
Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles
Hussain Abdul Hussain Ali
1
,
Taher Montadar Abas
2
,
Mahmood Omar Abdulkareem
2
,
Hammadi Yousif I
3
,
Alkanhel Reem
4
,
Muthanna Ammar
5
,
Publication type: Journal Article
Publication date: 2023-04-26
General Materials Science
Electrical and Electronic Engineering
General Engineering
General Computer Science
Abstract
Congestion in the world’s traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. The incorporation of contemporary technologies into transportation systems creates opportunities to significantly improve traffic prediction alongside modern academic challenges. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, a deep neural network architecture based on long short-term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers has been structured to build the deep neural network in order to predict the performance of the traffic flow in four distinct junctions, which has a great impact on the Internet of vehicles’ applications. The structure is composed of sixteen layers, five of which are GRU layers and one is a bi-directional LSTM layer. The dataset employed in this work involved four congested junctions. The dataset extended from November 1, 2016, to June 30, 2017. Cleaning and preprocessing operations were performed on the dataset before feeding it to the designed deep neural network in this paper. Results show that the suggested method produced comparable performance with respect to state-of-the-art approaches.
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Hussain A. H. A. et al. Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles // IEEE Access. 2023. Vol. 11. pp. 58516-58531.
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Hussain A. H. A., Taher M. A., Mahmood O. A., Hammadi Y. I., Alkanhel R., Muthanna A., Koucheryavy A. Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles // IEEE Access. 2023. Vol. 11. pp. 58516-58531.
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TY - JOUR
DO - 10.1109/access.2023.3270395
UR - https://doi.org/10.1109%2Faccess.2023.3270395
TI - Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles
T2 - IEEE Access
AU - Hussain, Abdul Hussain Ali
AU - Taher, Montadar Abas
AU - Mahmood, Omar Abdulkareem
AU - Hammadi, Yousif I
AU - Alkanhel, Reem
AU - Muthanna, Ammar
AU - Koucheryavy, Andrey
PY - 2023
DA - 2023/04/26 00:00:00
PB - IEEE
SP - 58516-58531
VL - 11
SN - 2169-3536
ER -
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@article{2023_Hussain,
author = {Abdul Hussain Ali Hussain and Montadar Abas Taher and Omar Abdulkareem Mahmood and Yousif I Hammadi and Reem Alkanhel and Ammar Muthanna and Andrey Koucheryavy},
title = {Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles},
journal = {IEEE Access},
year = {2023},
volume = {11},
publisher = {IEEE},
month = {apr},
url = {https://doi.org/10.1109%2Faccess.2023.3270395},
pages = {58516--58531},
doi = {10.1109/access.2023.3270395}
}
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