Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model
Abstract Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature se...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84710-8 |
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| _version_ | 1832585854441226240 |
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| author | Akshat Gaurav Brij B. Gupta Razaz Waheeb Attar Ahmed Alhomoud Varsha Arya Kwok Tai Chui |
| author_facet | Akshat Gaurav Brij B. Gupta Razaz Waheeb Attar Ahmed Alhomoud Varsha Arya Kwok Tai Chui |
| author_sort | Akshat Gaurav |
| collection | DOAJ |
| description | Abstract Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. These findings show how strong and successful our improved method is in precisely spotting drivers, thereby providing a useful instrument for safe and quick transportation systems. |
| format | Article |
| id | doaj-art-845e28e17afd4b9a806af249d6c2abc9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-845e28e17afd4b9a806af249d6c2abc92025-01-26T12:24:50ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84710-8Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest modelAkshat Gaurav0Brij B. Gupta1Razaz Waheeb Attar2Ahmed Alhomoud3Varsha Arya4Kwok Tai Chui5Ronin InstituteDepartment of Computer Science and Information Engineering, Asia UniversityManagement Department, College of Business Administration, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science, College of Science, Northern Border UniversityHong Kong Metropolitan UniversityHong Kong Metropolitan UniversityAbstract Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. These findings show how strong and successful our improved method is in precisely spotting drivers, thereby providing a useful instrument for safe and quick transportation systems.https://doi.org/10.1038/s41598-024-84710-8 |
| spellingShingle | Akshat Gaurav Brij B. Gupta Razaz Waheeb Attar Ahmed Alhomoud Varsha Arya Kwok Tai Chui Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model Scientific Reports |
| title | Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| title_full | Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| title_fullStr | Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| title_full_unstemmed | Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| title_short | Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| title_sort | driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model |
| url | https://doi.org/10.1038/s41598-024-84710-8 |
| work_keys_str_mv | AT akshatgaurav driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel AT brijbgupta driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel AT razazwaheebattar driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel AT ahmedalhomoud driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel AT varshaarya driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel AT kwoktaichui driveridentificationinadvancedtransportationsystemsusingospreyandsalpswarmoptimizedrandomforestmodel |