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<p>This study addresses the critical global challenge of road traffic fatalities (1.19 million annually, World Health Organization (WHO) 2023) by developing an innovative hybrid model that simultaneously evaluates both eco-driving and safety aspects—a significant advancement over existing approaches that treat these factors separately. Our methodology combines fuzzy logic classification with machine learning (Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Ensemble Learning) to analyze OBD-II data from 16 drivers, achieving remarkable results with 97.5% accuracy (MLP), 96.3% (SVM), and 97.2% (Ensemble). These performance metrics substantially improve upon previous works like (92% accuracy) and (94.2% accuracy), while our five-category classification system provides more granular insights than conventional binary safe/unsafe assessments. The practical impact is demonstrated through a 23.7% reduction in harsh braking incidents and 18.3% improvement in fuel efficiency among top-performing drivers, with real-time processing capabilities (&lt; 50&#xa0;ms latency) that enable immediate Advanced Driver Assistance Systems (ADAS) integration. This research contributes both methodologically—through its novel hybrid architecture combining interpretable fuzzy rules with machine learning precision—and practically by offering actionable tools to address the 90% of accidents attributed to driver behavior. The framework’s effectiveness across multiple validation scenarios and its potential for geographical adaptation as a significant advancement in driver behavior analysis and road safety improvement.</p>

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Towards Safer and Greener Roads: A Hybrid Eco-safe Driving Assessment Using Fuzzy Logic and Machine Learning Algorithms

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Süleyman Çeven,
  • Ahmet Albayrak,
  • Raif Bayır

摘要

This study addresses the critical global challenge of road traffic fatalities (1.19 million annually, World Health Organization (WHO) 2023) by developing an innovative hybrid model that simultaneously evaluates both eco-driving and safety aspects—a significant advancement over existing approaches that treat these factors separately. Our methodology combines fuzzy logic classification with machine learning (Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Ensemble Learning) to analyze OBD-II data from 16 drivers, achieving remarkable results with 97.5% accuracy (MLP), 96.3% (SVM), and 97.2% (Ensemble). These performance metrics substantially improve upon previous works like (92% accuracy) and (94.2% accuracy), while our five-category classification system provides more granular insights than conventional binary safe/unsafe assessments. The practical impact is demonstrated through a 23.7% reduction in harsh braking incidents and 18.3% improvement in fuel efficiency among top-performing drivers, with real-time processing capabilities (< 50 ms latency) that enable immediate Advanced Driver Assistance Systems (ADAS) integration. This research contributes both methodologically—through its novel hybrid architecture combining interpretable fuzzy rules with machine learning precision—and practically by offering actionable tools to address the 90% of accidents attributed to driver behavior. The framework’s effectiveness across multiple validation scenarios and its potential for geographical adaptation as a significant advancement in driver behavior analysis and road safety improvement.