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Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

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  • Yongfeng Ma

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    School of Transportation, Southeast University, Nanjing 211189, China)

  • Xin Gu

    (Beijing Key Laboratory of Traffic Engineering, The College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Ya’nan Yu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China)

  • Aemal J. Khattakc

    (330E Whittier Research Center, Nebraska Transportation Center, University of Nebraska-Lincoln, Lincoln, NE 68583-0851, USA)

  • Shuyan Chen

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China)

  • Kun Tang

    (School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness.

Suggested Citation

  • Yongfeng Ma & Xin Gu & Ya’nan Yu & Aemal J. Khattakc & Shuyan Chen & Kun Tang, 2021. "Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:766-:d:480446
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    References listed on IDEAS

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    1. Vladislav Krivda & Jan Petru & David Macha & Kristyna Plocova & David Fibich, 2020. "An Analysis of Traffic Conflicts as a Tool for Sustainable Road Transport," Sustainability, MDPI, vol. 12(17), pages 1-23, September.
    2. Archer, Kellie J. & Lemeshow, Stanley & Hosmer, David W., 2007. "Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4450-4464, May.
    3. Castillo-Manzano, José I. & Castro-Nuño, Mercedes, 2012. "Driving licenses based on points systems: Efficient road safety strategy or latest fashion in global transport policy? A worldwide meta-analysis," Transport Policy, Elsevier, vol. 21(C), pages 191-201.
    4. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
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    Cited by:

    1. Min Li & Wuhong Wang & Zhen Liu & Mingjun Qiu & Dayi Qu, 2022. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory," Sustainability, MDPI, vol. 14(11), pages 1-12, June.
    2. Zhenming Li & Siu Shing Man & Alan Hoi Shou Chan & Jianfang Zhu, 2021. "Integration of Theory of Planned Behavior, Sensation Seeking, and Risk Perception to Explain the Risky Driving Behavior of Truck Drivers," Sustainability, MDPI, vol. 13(9), pages 1-14, May.
    3. Maciej Kruszyna & Marta Matczuk-Pisarek, 2021. "The Effectiveness of Selected Devices to Reduce the Speed of Vehicles on Pedestrian Crossings," Sustainability, MDPI, vol. 13(17), pages 1-21, August.

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