IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v152y2024icp9-20.html
   My bibliography  Save this article

Examining risky driving behaviours: A comparative analysis of SUVs and other car types

Author

Listed:
  • Gupta, Akshay
  • Choudhary, Pushpa
  • Parida, Manoranjan

Abstract

The aim of the study was to analyze whether the driving behaviour changes with different types of cars (Hatchback, Sedan and Sports Utility Vehicle (SUV)), particularly on expressways. To achieve the study's objective, an exclusive DBQ was designed. Self-reported frequency of instances related to violations, mistakes and lapses were gathered which can collectively be used to study driver's behaviour in different circumstances. A total of 546 samples were collected through an online survey from drivers across India who possessed a valid driving license. Results of exploratory factor analysis confirmed two factor solution (errors and violations) and the same was verified by confirmatory factor analysis. With the help of structural equation modelling, individual risky driving score of each driver was calculated. Based on the calculated scores, drivers were clustered into different risk levels using K-Means clustering technique. Further, ANOVA test revealed that SUV drivers performed riskier behaviour more compared to other types of cars. To compare the relationships among different risk levels with drivers' demographics and driving characteristics, two ordered logit models were developed for SUV and Standard car drivers. The results indicated that drivers' age, crash history, and speeding behaviour were statistically significant predictors of risky driving behaviour. Comparison of risk levels between SUV and Standard cars with respect to different predictor variables presented various interesting findings. These findings would be helpful in understanding the differences among risky driving behaviour performed by drivers of different types of cars and to identify the potential road safety countermeasures.

Suggested Citation

  • Gupta, Akshay & Choudhary, Pushpa & Parida, Manoranjan, 2024. "Examining risky driving behaviours: A comparative analysis of SUVs and other car types," Transport Policy, Elsevier, vol. 152(C), pages 9-20.
    • Handle: RePEc:eee:trapol:v:152:y:2024:i:c:p:9-20
    DOI: 10.1016/j.tranpol.2024.04.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X24001094
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2024.04.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abay, Kibrom A. & Paleti, Rajesh & Bhat, Chandra R., 2013. "The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 74-89.
    2. Yanning Zhao & Toshiyuki Yamamoto, 2020. "Do People Drive Light Cars Carefully? A Comparative Study of Risky Driving Behaviors between Light Cars and Others," Energies, MDPI, vol. 13(7), pages 1-12, April.
    3. B. Claus & L. Warlop, 2022. "The Car Cushion Hypothesis: Bigger Cars Lead to More Risk Taking—Evidence from Behavioural Data," Journal of Consumer Policy, Springer, vol. 45(2), pages 331-342, June.
    Full references (including those not matched with items on IDEAS)

    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. Humpe, Andreas & Gössling, Stefan & Haustein, Sonja, 2022. "Car careers: A socio-psychological evaluation of aspirational automobile ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 156-166.
    2. Tong Zhu & Zishuo Zhu & Jie Zhang & Chenxuan Yang, 2021. "Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 18(21), pages 1-19, October.
    3. Feng Chen & Mingtao Song & Xiaoxiang Ma, 2019. "Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model," IJERPH, MDPI, vol. 16(14), pages 1-12, July.
    4. B. Claus & L. Warlop, 2022. "The Car Cushion Hypothesis: Bigger Cars Lead to More Risk Taking—Evidence from Behavioural Data," Journal of Consumer Policy, Springer, vol. 45(2), pages 331-342, June.
    5. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    6. Miguel Santolino & Mercedes Ayuso, 2020. "Number and severity of BI victims, assuming dependence between vehicles involved in the crash," IREA Working Papers 202018, University of Barcelona, Research Institute of Applied Economics, revised Dec 2020.
    7. Xiaojun Shao & Xiaoxiang Ma & Feng Chen & Mingtao Song & Xiaodong Pan & Kesi You, 2020. "A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    8. Abay, Kibrom A., 2013. "Examining pedestrian-injury severity using alternative disaggregate models," Research in Transportation Economics, Elsevier, vol. 43(1), pages 123-136.
    9. Jie Ma & Xin Ye & Cheng Shi, 2018. "Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    10. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2016. "Determining the role of bicycle sharing system infrastructure installation decision on usage: Case study of montreal BIXI system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 685-698.
    11. Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Effect of Imitation Phenomenon on Two-Lane Traffic Safety in Fog Weather," IJERPH, MDPI, vol. 16(19), pages 1-15, October.
    12. Abay, Kibrom A., 2015. "Investigating the nature and impact of reporting bias in road crash data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 31-45.
    13. Carina Goldbach & Deniz Kayar & Thomas Pitz & Jörn Sickmann, 2022. "Driving, Fast and Slow: An Experimental Investigation of Speed Choice and Information," SAGE Open, , vol. 12(2), pages 21582440221, April.
    14. Rahman, Moshiur & Yasmin, Shamsunnahar & Eluru, Naveen, 2019. "Controlling for endogeneity between bus headway and bus ridership: A case study of the Orlando region," Transport Policy, Elsevier, vol. 81(C), pages 208-219.
    15. Abay, Kibrom A., 2015. "Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models," Economics Letters, Elsevier, vol. 126(C), pages 51-56.
    16. Miguel Santolino & Luis Céspedes & Mercedes Ayuso, 2022. "The Impact of Aging Drivers and Vehicles on the Injury Severity of Crash Victims," IJERPH, MDPI, vol. 19(24), pages 1-16, December.
    17. Afghari, Amir Pooyan & Faghih Imani, Ahmadreza & Papadimitriou, Eleonora & van Gelder, Pieter & Hezaveh, Amin Mohamadi, 2021. "Disentangling the effects of unobserved factors on seatbelt use choices in multi-occupant vehicles," Journal of choice modelling, Elsevier, vol. 41(C).
    18. Wang, Kailai & Chen, Yu-Jen, 2020. "Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions," Journal of Transport Geography, Elsevier, vol. 82(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:eee:trapol:v:152:y:2024:i:c:p:9-20. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.