Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles
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 signi...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10108955/ |
| _version_ | 1826938518385983488 |
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| author | Abdul Hussain Ali Hussain Montadar Abas Taher Omar Abdulkareem Mahmood Yousif I. Hammadi Reem Alkanhel Ammar Muthanna Andrey Koucheryavy |
| author_facet | Abdul Hussain Ali Hussain Montadar Abas Taher Omar Abdulkareem Mahmood Yousif I. Hammadi Reem Alkanhel Ammar Muthanna Andrey Koucheryavy |
| author_sort | Abdul Hussain Ali Hussain |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T05:16:13Z |
| format | Article |
| id | doaj.art-767be1fcaf154f3f8b00e408c75c0388 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-02-17T18:58:14Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-767be1fcaf154f3f8b00e408c75c03882024-12-11T00:01:30ZengIEEEIEEE Access2169-35362023-01-0111585165853110.1109/ACCESS.2023.327039510108955Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of VehiclesAbdul Hussain Ali Hussain0Montadar Abas Taher1Omar Abdulkareem Mahmood2https://orcid.org/0000-0002-7541-3462Yousif I. Hammadi3https://orcid.org/0000-0002-1360-9005Reem Alkanhel4https://orcid.org/0000-0001-6395-4723Ammar Muthanna5https://orcid.org/0000-0003-0213-8145Andrey Koucheryavy6Department of Architectural Engineering, College of Engineering, University of Diyala, Baqubah, Diyala, IraqDepartment of Communications Engineering, College of Engineering, University of Diyala, Baqubah, Diyala, IraqDepartment of Communications Engineering, College of Engineering, University of Diyala, Baqubah, Diyala, IraqDepartment of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, Baqubah, Diyala, IraqDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Moscow, RussiaDepartment of Telecommunication Networks and Data Transmission, Bonch-Bruevich Saint Petersburg State University of Telecommunications, Saint Petersburg, RussiaCongestion 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.https://ieeexplore.ieee.org/document/10108955/Flow predictionBiLSTMdeep neural networkGRULSTMurban transportation |
| spellingShingle | Abdul Hussain Ali Hussain Montadar Abas Taher Omar Abdulkareem Mahmood Yousif I. Hammadi Reem Alkanhel Ammar Muthanna Andrey Koucheryavy Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles IEEE Access Flow prediction BiLSTM deep neural network GRU LSTM urban transportation |
| title | Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles |
| title_full | Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles |
| title_fullStr | Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles |
| title_full_unstemmed | Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles |
| title_short | Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles |
| title_sort | urban traffic flow estimation system based on gated recurrent unit deep learning methodology for internet of vehicles |
| topic | Flow prediction BiLSTM deep neural network GRU LSTM urban transportation |
| url | https://ieeexplore.ieee.org/document/10108955/ |
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