Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10379096/ |
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| author | Abdulrahman Alruban Hanan Abdullah Mengash Majdy M. Eltahir Nabil Sharaf Almalki Ahmed Mahmud Mohammed Assiri |
| author_facet | Abdulrahman Alruban Hanan Abdullah Mengash Majdy M. Eltahir Nabil Sharaf Almalki Ahmed Mahmud Mohammed Assiri |
| author_sort | Abdulrahman Alruban |
| collection | DOAJ |
| description | Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks. |
| format | Article |
| id | doaj-art-b15752037813474cabe2f65069752f94 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b15752037813474cabe2f65069752f942025-02-11T00:00:35ZengIEEEIEEE Access2169-35362024-01-0112175961760310.1109/ACCESS.2023.334903210379096Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation SystemsAbdulrahman Alruban0https://orcid.org/0000-0002-7295-5194Hanan Abdullah Mengash1https://orcid.org/0000-0002-4103-2434Majdy M. Eltahir2https://orcid.org/0000-0002-1810-4372Nabil Sharaf Almalki3Ahmed Mahmud4Mohammed Assiri5https://orcid.org/0000-0002-6367-2977Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majma’ah, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Jeddah, Saudi ArabiaDepartment of Special Education, College of Education, King Saud University, Riyadh, Saudi ArabiaResearch Center, Future University in Egypt, New Cairo, EgyptDepartment of Computer Science, College of Sciences and Humanities at Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi ArabiaIntelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.https://ieeexplore.ieee.org/document/10379096/Smart citiesintelligent transportation systemdeep learningtraffic managementfeature selection |
| spellingShingle | Abdulrahman Alruban Hanan Abdullah Mengash Majdy M. Eltahir Nabil Sharaf Almalki Ahmed Mahmud Mohammed Assiri Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems IEEE Access Smart cities intelligent transportation system deep learning traffic management feature selection |
| title | Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems |
| title_full | Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems |
| title_fullStr | Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems |
| title_full_unstemmed | Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems |
| title_short | Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems |
| title_sort | artificial hummingbird optimization algorithm with hierarchical deep learning for traffic management in intelligent transportation systems |
| topic | Smart cities intelligent transportation system deep learning traffic management feature selection |
| url | https://ieeexplore.ieee.org/document/10379096/ |
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