IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15484-d1271636.html
   My bibliography  Save this article

Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach

Author

Listed:
  • Zhaoming Chen

    (College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Wenyuan Xu

    (College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Youyang Qu

    (School of Architecture Civil Engineering, Heilongjiang University of Science and Technology, Harbin 150020, China)

Abstract

Unobserved heterogeneity is a major challenge in estimating reliable road safety models. The random parameters approach has been proven to be an effective way to account for unobserved heterogeneity but has rarely been used for crash frequency by severity level. In this paper, a fixed parameters model, a basic random parameters model, and an improved random parameters model, allowing for heterogeneity in the means and correlation of random parameters, are estimated and comparatively evaluated. To quantitatively analyze the impact of explanatory variables on the crash frequency of various severity levels, the calculating method of marginal effects for estimated models is proposed. The results indicate that (1) the basic random parameters model statistically outperforms the fixed parameters model, and the statistical fit can be further improved by introducing heterogeneous means and correlation of random parameters; (2) for the predictive performance, the basic random parameters model is more accurate than the fixed parameters model, and the improved random parameters model can further reduce the mean error, mean absolute error, and root mean square error by 40–100%, 3.7–8.3%, and 7.6–8.9%, respectively; (3) ignoring the unobserved heterogeneity or neglecting the heterogeneity in the means and correlation of random parameters may result in biased safety inferences, and the maximum bias of marginal effects can easily exceed 100 percent; and (4) the safety effects of explanatory variables are thoroughly discussed and the potential safety countermeasures are provided. The random parameters approach and the method for calculating marginal effects proposed in this study are expected to provide a new methodological alternative for the joint analysis of crash frequency by severity and should be helpful in uncovering the mechanism of crash occurrence and the resulting injury severity accurately.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15484-:d:1271636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15484/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15484/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maria Rella Riccardi & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    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. 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.
    4. 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.
    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. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    2. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. 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.
    4. 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.
    5. 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.
    6. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    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. Natalia Distefano & Salvatore Leonardi & Nilda Georgina Liotta, 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    9. Ke Wang & Xin Ye, 2021. "Development of alternative stochastic frontier models for estimating time-space prism vertices," Transportation, Springer, vol. 48(2), pages 773-807, April.
    10. Stefan Hochguertel & Henry Ohlsson, 2009. "Compensatory inter vivos gifts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 993-1023.
    11. Thanapong Champahom & Chamroeun Se & Sajjakaj Jomnonkwao & Tassana Boonyoo & Vatanavongs Ratanavaraha, 2023. "A Comparison of Contributing Factors between Young and Old Riders of Motorcycle Crash Severity on Local Roads," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    12. 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.
    13. 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.
    14. 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.
    15. Timothy J. Richards & Gordon J. Klein & Celine Bonnet & Zohra Bouamra-Mechemache, 2020. "Strategic Obfuscation and Retail Pricing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 57(4), pages 859-889, December.
    16. Muhammad Wisal Khattak & Hans De Backer & Pieter De Winne & Tom Brijs & Ali Pirdavani, 2024. "Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models," Sustainability, MDPI, vol. 16(4), pages 1-22, February.
    17. Katherine Fuller & Carola Grebitus, 2023. "Consumers' preferences and willingness to pay for coffee sustainability labels," Agribusiness, John Wiley & Sons, Ltd., vol. 39(4), pages 1007-1025, October.
    18. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    19. 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.
    20. 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.

    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:15:y:2023:i:21:p:15484-:d:1271636. 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.