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Enhancing Driving Safety Evaluation Through Correlation Analysis of Driver Behavior

Author

Listed:
  • Majun Fei

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Weiqi Zhou

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
    Research Institute of Engineering Technology, Jiangsu University, Zhenjiang 212013, China)

  • Hai Zhao

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Chaofeng Pan

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Dehua Shi

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
    Research Institute of Engineering Technology, Jiangsu University, Zhenjiang 212013, China)

  • Xinke An

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

Abstract

This paper presents a method for evaluating driving behavior safety based on real-world urban driving data collected from on-road experiments. The aim of this study is to develop a comprehensive and interpretable evaluation framework to improve the identification and correction of unsafe driving behaviors, particularly in urban electric vehicle applications. Five driving behavior indicators were selected: average speed, speed fluctuation difference, acceleration range, speeding frequency, and speed change frequency. The Frequent Pattern Growth (FP-growth) algorithm was applied to model and analyze the hidden relationships between these indicators. Principal component analysis (PCA) was used to determine the weight of each indicator, resulting in a comprehensive safety evaluation method based on the correlation of driving behaviors. The findings reveal that unsafe driving behaviors often occur in combination, with speeding, rapid acceleration, and speed change frequency frequently coexisting on the same road segment, collectively influencing driving safety. The proposed evaluation method was validated through comparative analysis of driving safety scores across different drivers, providing a useful reference for improving and correcting unsafe driving behaviors.

Suggested Citation

  • Majun Fei & Weiqi Zhou & Hai Zhao & Chaofeng Pan & Dehua Shi & Xinke An, 2025. "Enhancing Driving Safety Evaluation Through Correlation Analysis of Driver Behavior," Sustainability, MDPI, vol. 17(9), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4067-:d:1647064
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    References listed on IDEAS

    as
    1. Shaobo Ji & Ke Zhang & Guohong Tian & Zeting Yu & Xin Lan & Shibin Su & Yong Cheng, 2022. "Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car," Energies, MDPI, vol. 15(13), pages 1-23, June.
    2. Ruolan Fan & Gang Li & Yanan Wu, 2023. "State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    3. Victoria Gitelman & Etti Doveh, 2023. "Examining the Safety Impacts of High-Occupancy Vehicle Lanes: International Experience and an Evaluation of First Operation in Israel," Sustainability, MDPI, vol. 15(18), pages 1-25, September.
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