In the current paper, a model to predict the probability of accidents, injuries, and fatalities resulting from collisions between trains and vehicles at highway rail crossings is presented. Logistic regression and two databases maintained by the US Federal Railroad Administration (FRA) are used to build the models. These models prove more than an order of magnitude more accurate than a previous model developed by the FRA, which is currently used to compute the cost effectiveness of crossing upgrades. A declining trend in the likelihood of an accident that cannot be explained by changes in crossings of highways over time is discovered. Possible causes of this trend and test one of these possibilities are discussed. The model is used to compute both the cost per life saved from upgrading each crossing without gates and the trend over time in the number of crossings that are cost effective to upgrade.


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    Title :

    A model to predict the probability of highway rail crossing accidents


    Additional title:

    Prognosemodell der Unfallwahrscheinlichkeit an Eisenbahnübergängen


    Contributors:


    Publication date :

    2007


    Size :

    9 Seiten, 4 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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