How to Incorporate Accident Severity and Vehicle Occupancy into the Hot Spot Identification Process?

Document Type

Journal Article

Publication Date


Subject Area

operations - frequency, infrastructure - vehicle, ridership - forecasting, ridership - forecasting, mode - rail


Vehicle occupancy, Uncertainty, Risk analysis, Railroad grade crossings, Multinomials, Level crossings, Hotspot, Hot spot identification, Highway railroad grade crossings, Highway rail intersections, High accident locations, Grade crossings, Canada, Bayesian analysis, Accident severity, Accident risk forecasting, Accident rates, Accident prone locations, Accident frequency, Accident black spots


This paper introduces a Bayesian accident risk analysis framework that integrates accident frequency and its expected consequences into the hot spot identification process. The Bayesian framework allows the introduction of uncertainty not only in the accident frequency and severity model parameters but also in key variables such as vehicle occupancy levels and severity weighting factors. For modeling and estimating the severity levels of each individual involved in an accident, a Bayesian multinomial model is proposed. For modeling accident frequency, hierarchical Poisson models are used. How the framework can be implemented to compute alternative relative and absolute measures of total risk for hot spot identification is described. To illustrate the proposed approach, a group of highway–railway crossings from Canada is used as an application environment.