Analysis of crash injury severity on two trans-European transport network corridors in Spain using discrete-choice models and random forests.
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Objective: The objective of this paper is to identify the list of crash severity contributing factors and evaluate their impact on multiple-vehicle crashes on two high use Trans-European interurban, freight corridors in Spain (southern Europe): Madrid - Irn and Barcelona - Almera.Methods: We have used both logistic regression and random forests to identify crash severity predictors and estimate their impacts on crash outcomes. Although both statistical methods can provide useful information to help explain the safety implications of highway crashes, using both methods may further enable a more comprehensive understanding of this phenomenon. For this effort, we disaggregated the crash data into different crash types (i.e., head-on, angle, sideswipe and rear-end) and analyzed this data using roadway design elements, driver characteristics, and environmental factors. To identify the most important predictors of crash severity, we used the random forests data mining approach. We then used ordered logit models to estimate the effect of external factors on the severity of each crash type. Finally, we assessed the accuracy of the model estimates using bootstrap sampling.Results: The results of data mining analyses indicated that roadway design factors such as horizontal and vertical curvature, super elevation, and lane and shoulder width are among the most important factors associated with crash severity. The results of logistic regression show that the impact of the selected roadway element on the crash outcome is conditional on the crash type and the direction of the effects is not always consistent.Conclusions: The contribution of this paper to the existing literature is two-fold: the first important contribution of the paper is related to the safety analysis of two of the most important freight corridors in Spain and southern Europe. The second contribution of this paper is to address the existing gap in the literature relating to the comparison and compatibility of data mining and the logistic regression model.
