IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i11p3169-d237480.html
   My bibliography  Save this article

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

Author

Listed:
  • Ho-Chul Park

    (Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, USA)

  • Yang-Jun Joo

    (Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

  • Seung-Young Kho

    (Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

  • Dong-Kyu Kim

    (Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

  • Byung-Jung Park

    (Department of Transportation Engineering, Myongji University, Yongin 17058, Korea)

Abstract

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

Suggested Citation

  • Ho-Chul Park & Yang-Jun Joo & Seung-Young Kho & Dong-Kyu Kim & Byung-Jung Park, 2019. "Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3169-:d:237480
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/11/3169/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/11/3169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadia Solaro & Pier Ferrari, 2007. "Robustness of Parameter Estimation Procedures in Multilevel Models When Random Effects are MEP Distributed," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(1), pages 51-67, June.
    2. Alfonso Montella & Vittorio Marzano & Filomena Mauriello & Roberta Vitillo & Roberto Fasanelli & Mariano Pernetti & Francesco Galante, 2019. "Development of Macro-Level Safety Performance Functions in the City of Naples," Sustainability, MDPI, vol. 11(7), pages 1-21, March.
    3. Bruno ARPINO & Roberta VARRIALE, 2010. "Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 7-22.
    4. Maas, Cora J. M. & Hox, J.J.Joop J., 2004. "The influence of violations of assumptions on multilevel parameter estimates and their standard errors," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 427-440, June.
    5. Huang, Helai & Song, Bo & Xu, Pengpeng & Zeng, Qiang & Lee, Jaeyoung & Abdel-Aty, Mohamed, 2016. "Macro and micro models for zonal crash prediction with application in hot zones identification," Journal of Transport Geography, Elsevier, vol. 54(C), pages 248-256.
    6. Abdel-Aty, Mohamed & Lee, Jaeyoung & Siddiqui, Chowdhury & Choi, Keechoo, 2013. "Geographical unit based analysis in the context of transportation safety planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 62-75.
    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. Seunghoon Park & Dongwon Ko, 2020. "Investigating the Factors Influencing Pedestrian–Vehicle Crashes by Age Group in Seoul, South Korea: A Hierarchical Model," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    2. Lei Yang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Belgacem Bouallegue & Muhammad Faisal Javed & Nermin M. Salem, 2022. "Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestri," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    3. Wenlong Tao & Mahdi Aghaabbasi & Mujahid Ali & Abdulrazak H. Almaliki & Rosilawati Zainol & Abdulrhman A. Almaliki & Enas E. Hussein, 2022. "An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety," Sustainability, MDPI, vol. 14(4), pages 1-18, February.

    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. Ghadiri, Mehdi & Rassafi, Amir Abbas & Mirbaha, Babak, 2019. "The effects of traffic zoning with regular geometric shapes on the precision of trip production models," Journal of Transport Geography, Elsevier, vol. 78(C), pages 150-159.
    2. Tomislav Letnik & Katja Hanžič & Giuseppe Luppino & Matej Mencinger, 2022. "Impact of Logistics Trends on Freight Transport Development in Urban Areas," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    3. Jia Guo & Yusak Susilo & Constantinos Antoniou & Anna Pernestål Brenden, 2020. "Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    4. Chacon, Alvaro & Kausel, Edgar E. & Reyes, Tomas & Trautmann, Stefan, 2025. "Preventing algorithm aversion: People are willing to use algorithms with a learning label," Journal of Business Research, Elsevier, vol. 187(C).
    5. Martin Okolikj & Stephen Quinlan, 2016. "Context Matters: Economic Voting in the 2009 and 2014 European Parliament Elections," Politics and Governance, Cogitatio Press, vol. 4(1), pages 145-166.
    6. Paniagua, Victoria, 2022. "When clients vote for brokers: How elections improve public goods provision in urban slums," World Development, Elsevier, vol. 158(C).
    7. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    8. Ascensión Fumero & Rosario J. Marrero & Alicia Pérez-Albéniz & Eduardo Fonseca-Pedrero, 2021. "Adolescents’ Bipolar Experiences and Suicide Risk: Well-being and Mental Health Difficulties as Mediators," IJERPH, MDPI, vol. 18(6), pages 1-16, March.
    9. Francis L. Huang, 2022. "Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 101-125, February.
    10. Ahtasham Gul & Muhammad Mohsin & Muhammad Adil & Mansoor Ali, 2021. "A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-24, April.
    11. Huang, Helai & Song, Bo & Xu, Pengpeng & Zeng, Qiang & Lee, Jaeyoung & Abdel-Aty, Mohamed, 2016. "Macro and micro models for zonal crash prediction with application in hot zones identification," Journal of Transport Geography, Elsevier, vol. 54(C), pages 248-256.
    12. Giorgia Giovannetti & Giorgio Ricchiuti & Margherita Velucchi, 2013. "Location, internationalization and performance of firms in Italy: a multilevel approach," Applied Economics, Taylor & Francis Journals, vol. 45(18), pages 2665-2673, June.
    13. Christina Neeß, 2015. "Worauf achten Arbeitgeber im Auswahlprozess von Absolventen wirtschaftswissenschaftlicher Studiengänge? Ergebnisse eines faktoriellen Surveys [What do employers look for during the selection proces," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 48(4), pages 305-323, December.
    14. Shichen Huang & Chunfu Shao & Juan Li & Xiong Yang & Xiaoyu Zhang & Jianpei Qian & Shengyou Wang, 2020. "Feature Extraction and Representation of Urban Road Networks Based on Travel Routes," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    15. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    16. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    17. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).
    18. Asadi, Mehrnaz & Ulak, M. Baran & Geurs, Karst T. & Weijermars, Wendy, 2024. "A methodological framework to conduct joint zone-based analysis of traffic safety and accessibility," Journal of Transport Geography, Elsevier, vol. 118(C).
    19. Bao, Jie & Yang, Zhao & Zeng, Weili & Shi, Xiaomeng, 2021. "Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data," Journal of Transport Geography, Elsevier, vol. 94(C).
    20. Candel, Math J.J.M., 2007. "Empirical Bayes estimators of the random intercept in multilevel analysis: Performance of the classical, Morris and Rao version," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3027-3040, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:11:y:2019:i:11:p:3169-:d:237480. 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.