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Multi-vehicle Collisions involving Large Trucks on Highways: An Exploratory Discrete Outcome Analysis


  • E.I.T, Mouyid Islam
  • Hernandez, Salvador


Trucking industry is considered a driving force for logistic and supply chain systems which indirectly influences the national economy. So, any impedance in truck-flow or supply chain system eventually brings substantial consequences in terms of monetary values. As such, a growing concern related to large-truck (Gross Vehicle Weight Rating (GVWR) greater than 10,000 pounds) crashes has increased in recent years due to the potential economic impacts and level of injury severity sustained. With this in mind, this study aims to analyze the injury severities of multi-vehicle collisions involving large-trucks through an advanced econometric modeling approach to shed light on the contributing factors leading to large-truck crashes. Through a fused national crash datasets, we hope to provide a clearer understanding of the complex interactions of contributing factors (e.g., factors related to human (drivers), vehicle, and road-environment) influencing multi-vehicle crash outcomes. To capture these complexities using the national crash database and understand the underlying causal factor, discrete outcome models namely random parameter ordered probit and mixed logit (which accounts for observable factors) were estimated to predict the likelihood of five injury severity outcomes—fatal, incapacitating, non-incapacitating, possible injury, and no injury. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions of factors and that the effect of the some of the factors can vary across the observations.

Suggested Citation

  • E.I.T, Mouyid Islam & Hernandez, Salvador, 2012. "Multi-vehicle Collisions involving Large Trucks on Highways: An Exploratory Discrete Outcome Analysis," 53rd Annual Transportation Research Forum, Tampa, Florida, March 15-17, 2012 207113, Transportation Research Forum.
  • Handle: RePEc:ags:ndtr12:207113

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    References listed on IDEAS

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    6. Steven Stern, 1997. "Simulation-Based Estimation," Journal of Economic Literature, American Economic Association, vol. 35(4), pages 2006-2039, December.
    7. 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.
    8. Stewart Jones & David A. Hensher, 2007. "Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(7-8), pages 1193-1220.
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