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The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach

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  • Xiong, Yingge
  • Mannering, Fred L.

Abstract

One of the key aspects of graduated driver licensing programs is the new-driver experience gained in the presence of a guardian (a person providing mandatory supervision from the passenger seat). However, the effect that this guardian-supervising practice has on adolescent drivers’ crash-injury severity (should a crash occur) is not well understood. This paper seeks to provide insights into the injury-prevention effectiveness of guardian supervision by developing an appropriate econometric structure to account for the complex interactions that are likely to occur in the study of the heterogeneous effects of guardian supervision on crash-injury severities. As opposed to conventional heterogeneity models with standard distributional assumptions, this paper deals with the heterogeneous effects by accounting for the possible multivariate characteristics of parameter distributions in addition to allowing for multimodality, skewness and kurtosis. A Markov Chain Monte Carlo (MCMC) algorithm is developed for estimation and the permutation sampler proposed by Frühwirth-Schnatter (2001) is extended for model identification. The econometric analysis shows the presence of two distinct driving environments (defined by roadway geometric and traffic conditions). Model estimation results show that, in both of these driving environments, the presence of guardian supervision reduces the crash-injury severity, but in interestingly different ways. Based on the findings of this research, a case could easily be made for extending the time-requirement for guardian supervision in current graduated driver license programs.

Suggested Citation

  • Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
  • Handle: RePEc:eee:transb:v:49:y:2013:i:c:p:39-54
    DOI: 10.1016/j.trb.2013.01.002
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    Cited by:

    1. Karlõševa, Aljona & Nõmmann, Sulev & Nõmmann, Tea & Urbel-Piirsalu, Evelin & Budziński, Wiktor & Czajkowski, Mikołaj & Hanley, Nick, 2016. "Marine trade-offs: Comparing the benefits of off-shore wind farms and marine protected areas," Energy Economics, Elsevier, vol. 55(C), pages 127-134.
    2. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    3. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.

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