Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model
Akshat Gaurav1, Brij B Gupta2,3,4,5,6, Razaz Waheeb Attar7
1Ronin Institute, Montclair, NJ, USA.
Scientific Reports
|January 19, 2025
Summary
A new Random Forest model precisely identifies drivers using optimized feature selection and hyperparameter tuning. This advanced driver identification method enhances security and safety in transportation systems.
Area of Science:
- Computer Science
- Artificial Intelligence
- Transportation Engineering
Background:
- Driver identification is crucial for enhancing security, personalization, and safety in advanced transportation systems.
- Existing methods may not offer sufficient accuracy or efficiency for real-world applications.
Purpose of the Study:
- To propose a novel and highly accurate driver identification method.
- To optimize a Random Forest model for driver behavior analysis using advanced algorithms.
Main Methods:
- A Random Forest model was developed for driver identification based on driving behavior.
- Osprey Optimization Algorithm (OOA) was employed for optimal feature selection.
- Salp Swarm Optimization (SSO) was utilized for hyperparameter tuning.
Main Results:
- The proposed model achieved high performance metrics: 92% accuracy, 91% precision, 93% recall, and 92% F1-score.
- Outperformed traditional machine learning models like XGBoost, CatBoost, and Support Vector Machines.
Conclusions:
- The developed driver identification method demonstrates significant effectiveness and accuracy.
- This approach offers a valuable tool for improving safety and efficiency in transportation systems.
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