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<p>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.</p>

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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月19日

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  • Akshat Gaurav,
  • Brij B. Gupta,
  • Razaz Waheeb Attar,
  • Ahmed Alhomoud,
  • Varsha Arya,
  • Kwok Tai Chui

摘要

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.