Evaluating Factors Affecting Safety at Highway–Railway Grade Crossings

Abstract

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. Existing improvements to crossings include the installation of flashing lights or gates, the addition of extra warning devices such as four-quadrant barriers or wayside horns, and the enforcement of speed limits on the approaching highway. Statistical models are needed to ensure that countermeasures introduced at a given crossing are both cost-effective and practicable. However, in large part because of issues of colinearity, poor statistical significance, and parametric bias, many existing statistical models are simple in structure and feature few statistically significant explanatory variables. Accordingly, they fail to reflect the full gamut of factor inputs that explain variation in collision frequency at individual crossings over a given period of time. Before statistical models can be used to investigate the cost-effectiveness of specific countermeasures, models must be developed that more fully reflect the complex relationships that link a specific countermeasure to collision occurrence. This study presents a sequential modeling approach based on data mining and statistical methods to estimate the main and interactive effects of introducing countermeasures at individual grade crossings. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the model in decision support.