title: An alternative accident prediction model for highway-rail interfaces.
abstract: Safety levels at highway/rail interfaces continue to be of major concern despite an ever-increasing focus on improved design and appurtenance application practices. Despite the encouraging trend towards improved safety, accident frequencies remain high, many of which result in fatalities. More than half of these accidents occur at public crossings, where active warning devices (i.e. gates, lights, bells, etc.) are in place and functioning properly. This phenomenon speaks directly to the need to re-examine both safety evaluation (i.e. accident prediction) methods and design practices at highway-rail crossings. With respect to earlier developed accident prediction methods, the Peabody Dimmick Formula, the New Hampshire Index and the National Cooperative Highway Research Program (NCHRP) Hazard Index, all lack descriptive capabilities due to their limited number of explanatory variables. Further, each has unique limitations that are detailed in this paper. The US Department of Transportation's (USDOT) Accident Prediction Formula, which is most widely, also has limitations related to the complexity of the three-stage formula and its decline in accident prediction model accuracy over time. This investigation resulted in the development of an alternate highway-rail crossing accident prediction model, using negative binomial regression that shows great promise. The benefit to be gained through the application of this alternate model is (1) a greatly simplified, one-step estimation process; (2) comparable supporting data requirements and (3) interpretation of both the magnitude and direction of the effect of the factors found to significantly influence highway-rail crossing accident frequencies.
authors.affiliation: Western Transportation Institute, Montana State University, Bozeman 59717-3910, USA.
authors.forename: Ross D
authors.initials: RD
authors.lastname: Austin
authors.forename: Jodi L
authors.initials: JL
authors.lastname: Carson
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D011482:Protective Devices; D011890:Railroads / Q000706:statistics & numerical data*; D017751:Safety Management