IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p766-d480446.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/766/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/2/766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rahman, Shaikh Moksadur, 2020. "Relationship between Job Satisfaction and Turnover Intention: Evidence from Bangladesh," Asian Business Review, Asian Business Consortium, vol. 10(2), pages 99-108.
    2. Wang Kai, 2019. "Towards a Taxonomy of Idea Generation Techniques," Foundations of Management, Sciendo, vol. 11(1), pages 65-80, January.
    3. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    4. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    5. Naveena Prakasam & Louisa Huxtable-Thomas, 2021. "Reddit: Affordances as an Enabler for Shifting Loyalties," Information Systems Frontiers, Springer, vol. 23(3), pages 723-751, June.
    6. Colin Jerolmack & Alexandra K. Murphy, 2019. "The Ethical Dilemmas and Social Scientific Trade-offs of Masking in Ethnography," Sociological Methods & Research, , vol. 48(4), pages 801-827, November.
    7. Valeriy Makarov & Albert Bakhtizin, 2014. "The Estimation Of The Regions’ Efficiency Of The Russian Federation Including The Intellectual Capital, The Characteristics Of Readiness For Innovation, Level Of Well-Being, And Quality Of Life," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 9-30.
    8. Zhao, Jing & Knoop, Victor L. & Wang, Meng, 2020. "Two-dimensional vehicular movement modelling at intersections based on optimal control," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 1-22.
    9. Kristine Edgar Danielyan & Samvel Grigoriy Chailyan, 2019. "Delineation of Effectors Impact on The Human Brain Derived Phosphoribosylpyrophosphate Synthetase-1 Activity," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 24(1), pages 17918-17926, December.
    10. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    11. Mohammad AL-Zoubi, 2018. "The Role of Technology, Organization, and Environment Factors in Enterprise Resource Planning Implementation Success in Jordan," International Business Research, Canadian Center of Science and Education, vol. 11(8), pages 48-65, August.
    12. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    13. Nicole D. Sintov & P. Wesley Schultz, 2017. "Adjustable Green Defaults Can Help Make Smart Homes More Sustainable," Sustainability, MDPI, vol. 9(4), pages 1-12, April.
    14. Hwang, ShinYoung & Kim Seongcheol, 2017. "What triggers the use of mIM service provider’s sequel O2O service extensions?," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168494, International Telecommunications Society (ITS).
    15. Sana Sadiq & Khadija Anasse & Najib Slimani, 2022. "The impact of mobile phones on high school students: connecting the research dots," Technium Social Sciences Journal, Technium Science, vol. 30(1), pages 252-270, April.
    16. Maude Hasbi & Antoine Dubus, 2019. "Determinants of Mobile Broadband Use in Developing Economies: Evidence from Sub-Saharan Africa," Working Papers hal-02264651, HAL.
    17. Jascha-Alexander Koch & Michael Siering, 2019. "The recipe of successful crowdfunding campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 661-679, December.
    18. Martins, José & Costa, Catarina & Oliveira, Tiago & Gonçalves, Ramiro & Branco, Frederico, 2019. "How smartphone advertising influences consumers' purchase intention," Journal of Business Research, Elsevier, vol. 94(C), pages 378-387.
    19. Retina Rimal & Chris Papadopoulos, 2016. "The mental health of sexually trafficked female survivors in Nepal," International Journal of Social Psychiatry, , vol. 62(5), pages 487-495, August.
    20. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:766-:d:480446. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.