RISK-BASED MODEL FOR IDENTIFYING HIGHWAY-RAIL GRADE CROSSING BLACKSPOTS

A risk-based model is presented for identifying highway-rail grade crossing blackspots. This model consists of two prediction components: collision frequency and collision consequence. A graphic approach is adopted to identify crossings with unacceptable risks (high expected frequencies or consequences or both). These crossings are referred to as blackspots. The model was applied to Canadian inventory and collision occurrence data for the period 1997-2001. Poisson and negative binomial (NB) frequency prediction expressions were developed for crossings with three types of warning devices (signs, flashing lights, and gates). The NB model was found to provide a better fit to the collision frequency data. A weighted consequence score was introduced to represent combined collision severity. The weights used in this combined consequence score were obtained from insurance claims. An NB expression was developed for the collision consequence model. The spatial distribution of blackspots is discussed with respect to the type of warning device, upgrades in warning device, geographic location, and historical collision occurrence. A geographic information system platform was developed for the Ontario region and used to illustrate the spatial pattern of expected and historical collision frequency and associated blackspots.