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A novel hybrid supervised machine learning model with metaheuristic optimization algorithm for prediction of driver
Yashuvanthra Dhevi S1, Sathya Narayana Sharma K2, Prakash Sandhya1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632014, India.
Scientific Reports
|May 11, 2026
Summary
This study introduces a new hybrid machine learning model, CatBoost+Stacking, to accurately predict driver concentration and improve road safety. This advanced technique helps monitor driver behavior and reduce accidents in urban environments.
Area of Science:
- Computer Science
- Artificial Intelligence
- Transportation Engineering
Background:
- Urban expansion increases road congestion, challenging driver attention and road safety.
- Monitoring driver focus is crucial for preventing accidents.
Purpose of the Study:
- To investigate advanced machine learning techniques for assessing and predicting driver focus.
- To develop a novel hybrid model for enhanced driver concentration prediction.
Main Methods:
- Evaluated multiple machine learning models including Logistic Regression, SVM, Random Forest, and CatBoost.
- Employed a metaheuristic optimization algorithm for hyperparameter tuning.
- Introduced a novel hybrid model: CatBoost+Stacking.
Main Results:
- The CatBoost+Stacking model demonstrated superior accuracy in predicting driver concentration levels.
- This hybrid model outperformed existing baseline models in performance.
- The study analyzed metrics like concentration scores, reaction times, and stress management.
Conclusions:
- The CatBoost+Stacking model offers a more effective approach to monitoring driver behavior.
- Findings provide insights for proactive road safety strategies and accident reduction.
- Hybrid machine learning models hold transformative potential for safer driving environments.
