Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)
Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Art...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001452 |
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| author | Muhammad Waqas Sagheer Abbas Umer Farooq Muhammad Adnan Khan Munir Ahmad Nasir Mahmood |
| author_facet | Muhammad Waqas Sagheer Abbas Umer Farooq Muhammad Adnan Khan Munir Ahmad Nasir Mahmood |
| author_sort | Muhammad Waqas |
| collection | DOAJ |
| description | Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem. |
| format | Article |
| id | doaj-art-c9bc6108d3c44cf891b1dad9991a8d90 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-c9bc6108d3c44cf891b1dad9991a8d902024-12-15T06:14:52ZengElsevierEgyptian Informatics Journal1110-86652024-12-0128100582Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)Muhammad Waqas0Sagheer Abbas1Umer Farooq2Muhammad Adnan Khan3Munir Ahmad4Nasir Mahmood5Department of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanDepartment of Computer Science, Prince Mohammad Bin Fahd University, Al Khobar, Dhahran 34754, Saudi Arabia; Corresponding authors.Department of Computing, Faculty of Engineering Science and Technology, Hamdard University, Karachi, Sindh, PakistanDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si 13120, Republic of Korea; Corresponding authors.Department of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan; College of Informatics, Korea University, Seoul 02841, Republic of KoreaDepartment of Computer Science UET, Lahore 54000, PakistanUrban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem.http://www.sciencedirect.com/science/article/pii/S1110866524001452Long Short-Term MemoryRecurrent Neural Network (RNN)Explainable Artificial Intelligence (EAI)Smart CityIoTArtificial Intelligence |
| spellingShingle | Muhammad Waqas Sagheer Abbas Umer Farooq Muhammad Adnan Khan Munir Ahmad Nasir Mahmood Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) Egyptian Informatics Journal Long Short-Term Memory Recurrent Neural Network (RNN) Explainable Artificial Intelligence (EAI) Smart City IoT Artificial Intelligence |
| title | Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) |
| title_full | Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) |
| title_fullStr | Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) |
| title_full_unstemmed | Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) |
| title_short | Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) |
| title_sort | autonomous vehicles congestion model a transparent lstm based prediction model corporate with explainable artificial intelligence eai |
| topic | Long Short-Term Memory Recurrent Neural Network (RNN) Explainable Artificial Intelligence (EAI) Smart City IoT Artificial Intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001452 |
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