Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jul 23;16(14):2632.
doi: 10.3390/ijerph16142632.

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

Feng Chen  1 , Mingtao Song  1 , Xiaoxiang Ma  2
Affiliations

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

Feng Chen et al. Int J Environ Res Public Health. .

Abstract

The existing studies on drivers' injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers' injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

Keywords: injury severity; random parameter bivariate ordered probit; rear-end crash.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary statistics for driver-related, vehicle-related and other independent variables.
See this image and copyright information in PMC

References

    1. World Health Organization Global status report on road safety 2015. Inj. Prev. 2015;15:286. - PubMed
    1. Dong C., Clarke D.B., Nambisan S.S., Huang B. Analyzing injury crashes using random-parameter bivariate regression models. Transp. A Transp. Sci. 2016;12:794–810. doi: 10.1080/23249935.2016.1177134. - DOI
    1. Chen C., Zhang G., Tarefder R., Ma J., Wei H., Guan H. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accid. Anal. Prev. 2015;80:76–88. doi: 10.1016/j.aap.2015.03.036. - DOI - PubMed
    1. Singh S. Driver attributes and rear-end crash involvement propensity. National Technical Information Service; Springfield, Virginia, VA, USA: Mar, 2003. DOT HS 809 540.
    1. Zhang K., Hassan M. Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes. J. Adv. Transport. 2019;2019:9. doi: 10.1155/2019/4126102. - DOI

Publication types