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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

In this paper, the application of transportation systems in real time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system....

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Main Authors: Ayman Khallel Al-ani, Shams Ul Arfeen Laghari, Hariprasath Manoharan, Shitharth Selvarajan, Mueen Uddin
Format: Article
Language:English
English
Published: Tech Science Press 2023
Subjects:
QA75.5-76.95 Electronic computers. Computer science
TA1001-1280 Transportation engineering
Online Access:https://eprints.ums.edu.my/id/eprint/38446/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38446/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38446/
https://doi.org/10.32604/cmc.2023.038534
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spelling my.ums.eprints.384462024-03-05T02:36:41Z https://eprints.ums.edu.my/id/eprint/38446/ Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm Ayman Khallel Al-ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin QA75.5-76.95 Electronic computers. Computer science TA1001-1280 Transportation engineering In this paper, the application of transportation systems in real time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. Therefore, the combined objective case studies are examined as minimization and maximization criteria, thus increasing the efficiency of the proposed method. Finally, four scenarios are chosen to investigate the projected design’s effectiveness. In all simulated metrics, the proposed approach provides better operational outcomes for an average percentage of 97, thereby reducing the amount of traffic in real-time conditions. Tech Science Press 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38446/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38446/2/FULL%20TEXT.pdf Ayman Khallel Al-ani and Shams Ul Arfeen Laghari and Hariprasath Manoharan and Shitharth Selvarajan and Mueen Uddin (2023) Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm. Computers, Materials & Continua, 76. pp. 2261-2279. ISSN 1546-2226 https://doi.org/10.32604/cmc.2023.038534
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA75.5-76.95 Electronic computers. Computer science
TA1001-1280 Transportation engineering
spellingShingle QA75.5-76.95 Electronic computers. Computer science
TA1001-1280 Transportation engineering
Ayman Khallel Al-ani
Shams Ul Arfeen Laghari
Hariprasath Manoharan
Shitharth Selvarajan
Mueen Uddin
Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
description In this paper, the application of transportation systems in real time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. Therefore, the combined objective case studies are examined as minimization and maximization criteria, thus increasing the efficiency of the proposed method. Finally, four scenarios are chosen to investigate the projected design’s effectiveness. In all simulated metrics, the proposed approach provides better operational outcomes for an average percentage of 97, thereby reducing the amount of traffic in real-time conditions.
format Article
author Ayman Khallel Al-ani
Shams Ul Arfeen Laghari
Hariprasath Manoharan
Shitharth Selvarajan
Mueen Uddin
author_facet Ayman Khallel Al-ani
Shams Ul Arfeen Laghari
Hariprasath Manoharan
Shitharth Selvarajan
Mueen Uddin
author_sort Ayman Khallel Al-ani
title Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
title_short Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
title_full Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
title_fullStr Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
title_full_unstemmed Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
title_sort improved transportation model with internet of things using artificial intelligence algorithm
publisher Tech Science Press
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/38446/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38446/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38446/
https://doi.org/10.32604/cmc.2023.038534
_version_ 1793154685356474368
score 13.211869

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