IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14342-d961197.html
   My bibliography  Save this article

A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks

Author

Listed:
  • Syed As-Sadeq Tahfim

    (School of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China)

  • Yan Chen

    (School of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China)

Abstract

The significance of large trucks for the expansion and well-being of the economy is a well-established fact. However, crashes involving large trucks significantly threaten the overall safety on the roads. Moreover, a significant proportion of fatal crashes involving large trucks occurs on interstate roadways in the United States. However, not many studies have focused on the heterogeneous effects of the contributory factors on injury outcomes of interstate crashes involving large trucks. The current study explores the application of a k-prototypes clustering-based mixed logit model to identify and analyze the heterogeneous effects of contributory factors on injury outcomes in different scenarios of interstate crashes involving large trucks. Data from six years of crashes involving large trucks that occurred on interstate roadways in the state of Pennsylvania, US, were used in this study. The list of contributory factors included the following: drivers’ demographics and behaviors; crash characteristics; vehicle-related factors; location and roadway attributes; and environmental factors. The results indicated that some of the contributory factors were significant for all scenarios of interstate crashes involving large trucks. However, the magnitude of those factors’ effects varied across scenarios. Moreover, some of the contributory factors were exclusive to certain scenarios of interstate crashes involving large trucks. Lastly, the identification of random parameters in the cluster-based models indicated that a cluster-based mixed logit model is a more effective approach for accurately estimating the effects of contributory factors on injury outcomes in large-truck interstate crashes. The empirical findings of this study can be used to develop more robust traffic laws and safety measures to reduce the frequency and severity of injury in different scenarios of interstate crashes involving large trucks.

Suggested Citation

  • Syed As-Sadeq Tahfim & Yan Chen, 2022. "A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14342-:d:961197
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14342/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14342/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    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. Ortega, David L. & Wang, H. Holly & Wu, Laping & Hong, Soo Jeong, 2015. "Retail channel and consumer demand for food quality in China," China Economic Review, Elsevier, vol. 36(C), pages 359-366.
    2. Pereira, Pedro & Ribeiro, Tiago, 2011. "The impact on broadband access to the Internet of the dual ownership of telephone and cable networks," International Journal of Industrial Organization, Elsevier, vol. 29(2), pages 283-293, March.
    3. Choi, Andy S., 2013. "Nonmarket values of major resources in the Korean DMZ areas: A test of distance decay," Ecological Economics, Elsevier, vol. 88(C), pages 97-107.
    4. Doherty, Edel & Campbell, Danny, 2011. "Demand for improved food safety and quality: a cross-regional comparison," 85th Annual Conference, April 18-20, 2011, Warwick University, Coventry, UK 108791, Agricultural Economics Society.
    5. Abdurrahman B. Aydemir & Erkan Duman, 2021. "Migrant Networks and Destination Choice: Evidence from Moves across Turkish Provinces," Koç University-TUSIAD Economic Research Forum Working Papers 2109, Koc University-TUSIAD Economic Research Forum.
    6. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    7. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    8. Veneziani, Mario & Sckokai, Paolo & Moro, Daniele, 2012. "Consumers’ willingness to pay for a functional food," 2012 First Congress, June 4-5, 2012, Trento, Italy 124101, Italian Association of Agricultural and Applied Economics (AIEAA).
    9. Kesternich, Iris & Heiss, Florian & McFadden, Daniel & Winter, Joachim, 2013. "Suit the action to the word, the word to the action: Hypothetical choices and real decisions in Medicare Part D," Journal of Health Economics, Elsevier, vol. 32(6), pages 1313-1324.
    10. Jianhua Wang & Jiaye Ge & Yuting Ma, 2018. "Urban Chinese Consumers’ Willingness to Pay for Pork with Certified Labels: A Discrete Choice Experiment," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    11. Qian, Lixian & Grisolía, Jose M. & Soopramanien, Didier, 2019. "The impact of service and government-policy attributes on consumer preferences for electric vehicles in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 70-84.
    12. Scaccia, Luisa & Marcucci, Edoardo & Gatta, Valerio, 2023. "Prediction and confidence intervals of willingness-to-pay for mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 54-78.
    13. Hyunjoo Lee & Misuk Lee & Sesil Lim, 2018. "Do Consumers Care about the Energy Efficiency of Buildings? Understanding Residential Choice Based on Energy Performance Certificates," Sustainability, MDPI, vol. 10(11), pages 1-18, November.
    14. Shin, Jungwoo & Hwang, Won-Sik, 2017. "Consumer preference and willingness to pay for a renewable fuel standard (RFS) policy: Focusing on ex-ante market analysis and segmentation," Energy Policy, Elsevier, vol. 106(C), pages 32-40.
    15. Don Fullerton & Li Gan & Miwa Hattori, 2015. "A model to evaluate vehicle emission incentive policies in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(1), pages 79-108, January.
    16. Liu, Ruifeng & ,, 2021. "What We Can Learn from the Interactions of Food Traceable Attributes? a Case Study of Fuji Apple in China," 2021 Conference, August 17-31, 2021, Virtual 315916, International Association of Agricultural Economists.
    17. Frick, Bernd & Barros, Carlos Pestana & Prinz, Joachim, 2010. "Analysing head coach dismissals in the German "Bundesliga" with a mixed logit approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 151-159, January.
    18. Meredith Fowlie, 2010. "Emissions Trading, Electricity Restructuring, and Investment in Pollution Abatement," American Economic Review, American Economic Association, vol. 100(3), pages 837-869, June.
    19. Cho, Woohyun & Windle, Robert J. & Dresner, Martin E., 2017. "The impact of operational exposure and value-of-time on customer choice: Evidence from the airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 455-471.
    20. Ida, Takanori & Goto, Rei & Takahashi, Yuko & Nishimura, Shuzo, 2011. "Can economic-psychological parameters predict successful smoking cessation?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(3), pages 285-295, May.

    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:14342-:d:961197. 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.