A Model for Evaluating Countermeasures at Highway–Railway Grade Crossings

Current collision prediction models fail to account for the full spectrum of relevant factors affecting the number of collisions at specific highway– railway grade crossings. A number of reasons contribute to this failure, including biases in model parameters resulting from collinearity in the model inputs, absence of important variables in the prediction model caused by lack of statistical significance, the inability of models to consider higher-order interactions, and the presence of unexplained variation in the prediction estimates. These problems have compromised the use of collision prediction models in decisions concerning the development and evaluation of cost-effective safety treatments or countermeasures for application at specific crossings. This paper introduces a stratified collision prediction model for highway–railway grade crossings. The development of this model involves three steps: ( a) crossing inventory variables are expressed in terms of a limited number of orthogonal (nonlinear) underlying attributes or factors; ( b) factor scores are estimated for each crossing and factor, and these scores are used as “seed points” in a subsequent clustering exercise to yield groups or clusters of crossings with similar underlying attributes; and ( c) for each cluster, separate collision prediction models are developed and include important treatment input variables of interest to decision makers and planners. The paper describes an application of a stratified collision prediction model to Canadian highway–railway grade crossing inventory and collision occurrence data for the period 1993 to 2001. The usefulness of the model in estimating collision reduction benefits of selected treatments is illustrated with reference to two countermeasure strategies: upgrades in the type of warning device and the removal of whistle prohibition.