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Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers

Author

Listed:
  • Chien-Hung Wei

    (Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan City 701, Taiwan)

  • Ying Lee

    (Department of Supply Chain Management, National Kaohsiung University of Science and Technology, Kaohsiung 811, Taiwan)

  • Yu-Wen Luo

    (Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan City 701, Taiwan)

  • Jyun-Jie Lu

    (Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan City 701, Taiwan)

Abstract

Economic globalization and the internet economy have resulted in a dramatic increase in freight transportation. Traffic crashes involving trucks usually result in severe losses and casualties. The fatality and injury rates for heavy truck accidents have been 10 times higher than for sedans in Taiwan in recent years. Thus, understanding driving behavior and risk are important for freight carriers. Since personality traits may result in different driving behaviors, the main objective of this study is to apply artificial neural network (ANN) models to predict the frequency of aberrant driving behavior and the risk level of each driver according to drivers’ personality traits. In this case study, relevant information on truck drivers’ personality traits and their tendency to engage in aberrant driving behavior are collected by using respectively a questionnaire and a fleet surveillance system from a truck company. A relative risk level evaluation mechanism is developed considering the frequency and distribution of aberrant driving behavior. The Jenks natural breaks optimization method and the elbow method are adopted to optimally classify 40 truck drivers into 4 aberrant driving behavior levels and 5 driving risk levels. It was found that 5% of drivers were at the highest aberrant driving behavior level, and 7.5% of drivers were at the highest driving risk level. Based on the results, the proposed models show good and stable predictive performance, especially for the class of drivers with excessive rotation speed, hard acceleration, excessive rotation speed, hard deceleration, and driving risk. With the proposed models, the predictive class for aberrant driving behavior and driving risk can be determined by plugging in a driver’s personality traits before or after employment. Based on the prediction results, the manager of a transportation company could plan the training program for each driver to reduce the aberrant driving behavior occurrence.

Suggested Citation

  • Chien-Hung Wei & Ying Lee & Yu-Wen Luo & Jyun-Jie Lu, 2021. "Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers," IJERPH, MDPI, vol. 18(9), pages 1-18, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4601-:d:543929
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    References listed on IDEAS

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    1. Javadreza Vahedi & Afshin Shariat Mohaymany & Zahra Tabibi & Milad Mehdizadeh, 2018. "Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers," IJERPH, MDPI, vol. 15(8), pages 1-17, August.
    2. Luis Montoro & Sergio Useche & Francisco Alonso & Boris Cendales, 2018. "Work Environment, Stress, and Driving Anger: A Structural Equation Model for Predicting Traffic Sanctions of Public Transport Drivers," IJERPH, MDPI, vol. 15(3), pages 1-12, March.
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    Cited by:

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    2. Nattawut Pumpugsri & Wanchai Rattanawong & Varin Vongmanee, 2023. "Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    3. Weiwei Qi & Shufang Zhu & Jinsong Hu, 2022. "Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System," Sustainability, MDPI, vol. 14(17), pages 1-17, September.

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