IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p13821-d952247.html
   My bibliography  Save this article

A Random Parameters Approach to Investigate Injury Severity of Two-Vehicle Crashes at Intersections

Author

Listed:
  • Mostafa Sharafeldin

    (Wyoming Technology Transfer Center (WYT2/LTAP), Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA)

  • Ahmed Farid

    (Department of Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA)

  • Khaled Ksaibati

    (Wyoming Technology Transfer Center (WYT2/LTAP), Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA)

Abstract

Roadway intersections are crash-prone locations and, hence, ensuring the safety of road users at intersections has been a major concern for transportation professionals. It is critical to identify the risk factors that contribute to severe crashes at intersections to implement the appropriate countermeasures. Greater emphasis is needed on two-vehicle crashes since they represent the majority of intersection crashes. In this study, a random parameter ordinal probit model was developed to estimate the contributing factors of injury severity of two-vehicle crashes at intersections. Nine years of intersection crash data in Wyoming were analyzed in this model. The study involved the investigation of the influence of a set of intersection, drivers, environmental, and crash characteristics on crash injury severity. The results demonstrated urban and signalized intersections were related to lower severity levels. In addition, higher pavement friction is more likely to be associated with less severe crashes. Crashes that involved drivers who are females or impaired and crashes on weekends were associated with higher severity levels. Intersection crashes that occurred on non-dry road surfaces, in adverse weather conditions, or that involved large vehicles, or out-of-state drivers were less likely to be severe.

Suggested Citation

  • Mostafa Sharafeldin & Ahmed Farid & Khaled Ksaibati, 2022. "A Random Parameters Approach to Investigate Injury Severity of Two-Vehicle Crashes at Intersections," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13821-:d:952247
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/13821/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/13821/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mostafa Sharafeldin & Omar Albatayneh & Ahmed Farid & Khaled Ksaibati, 2022. "A Bayesian Approach to Examine the Impact of Pavement Friction on Intersection Safety," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    2. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    3. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
    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. Masayoshi Tanishita & Yuta Sekiguchi, 2023. "Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-Vehicle Crashes," Sustainability, MDPI, vol. 15(17), pages 1-17, September.
    2. Yoshifumi Wada & Yasushi Asami & Kimihiro Hino & Hayato Nishi & Shino Shiode & Narushige Shiode, 2023. "Road Junction Configurations and the Severity of Traffic Accidents in Japan," Sustainability, MDPI, vol. 15(3), pages 1-17, February.
    3. Zhaoming Chen & Wenyuan Xu & Youyang Qu, 2023. "Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach," Sustainability, MDPI, vol. 15(21), pages 1-25, October.

    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. Mostafa Sharafeldin & Ahmed Farid & Khaled Ksaibati, 2022. "Injury Severity Analysis of Rear-End Crashes at Signalized Intersections," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    2. Liu, Qiang & Homma, Riken & Iki, Kazuhisa, 2020. "Evaluating cyclists’ perception of satisfaction using 360° videos," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 205-213.
    3. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    4. Bhat, Chandra R. & Sardesai, Rupali, 2006. "The impact of stop-making and travel time reliability on commute mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 709-730, November.
    5. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    6. Barbour, Natalia & Menon, Nikhil & Zhang, Yu & Mannering, Fred, 2019. "Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns," Transport Policy, Elsevier, vol. 80(C), pages 86-93.
    7. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    8. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    9. Richards, Timothy J. & Hamilton, Stephen F. & Yonezawa, Koichi, 2018. "Retail Market Power in a Shopping Basket Model of Supermarket Competition," Journal of Retailing, Elsevier, vol. 94(3), pages 328-342.
    10. Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    11. Ufer, Danielle J. & Ortega, David L. & Wolf, Christopher A. & McKendree, Melissa & Swanson, Janice, 2022. "Getting past the gatekeeper: Key motivations of dairy farmer intent to adopt animal health and welfare-improving biotechnology," Food Policy, Elsevier, vol. 112(C).
    12. Stefan Hochguertel & Henry Ohlsson, 2009. "Compensatory inter vivos gifts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 993-1023.
    13. Staus, Alexander, 2008. "Standard and Shuffled Halton Sequences in a Mixed Logit Model," Working Papers 93856, Universitaet Hohenheim, Institute of Agricultural Policy and Agricultural Markets.
    14. Chae, Dasol & Jung, Jaeyoung & Sohn, Keemin, 2018. "Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables," Journal of choice modelling, Elsevier, vol. 26(C), pages 64-79.
    15. Gustavo Ahumada & Victor Iturra & Mauricio Sarrias, 2020. "We Do Not Have the Same Tastes! Evaluating Individual Heterogeneity in the Preferences for Amenities," Journal of Happiness Studies, Springer, vol. 21(1), pages 53-74, January.
    16. Nguyen, Ly & Gao, Zhifeng & Anderson, James L., 2022. "Regulating menu information: What do consumers care and not care about at casual and fine dining restaurants for seafood consumption?," Food Policy, Elsevier, vol. 110(C).
    17. Lei Kang & Jon Fricker, 2013. "Bicyclist commuters’ choice of on-street versus off-street route segments," Transportation, Springer, vol. 40(5), pages 887-902, September.
    18. Hensher, David A., 2008. "Empirical approaches to combining revealed and stated preference data: Some recent developments with reference to urban mode choice," Research in Transportation Economics, Elsevier, vol. 23(1), pages 23-29, January.
    19. Milhan Moomen & Amirarsalan Mehrara Molan & Khaled Ksaibati, 2023. "A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    20. Sanjog Misra, 2005. "Generalized Reverse Discrete Choice Models," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 175-200, June.

    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:14:y:2022:i:21:p:13821-:d:952247. 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.