At a European level, road accident fatalities on “two-lane rural roads” constitute up to 56% of the total number of fatalities caused by road accidents. This paper presents a comprehensive procedure starting from a low-cost data survey and leading up to the implementation of an Accident Prediction Model (APM) specifically designed for two lane rural roads. Firstly, significant variables were identified for the segmentation of highways into homogeneous sections. More specifically, research efforts is used to divide the roads sample into segments in which all the highway characteristics (exposure, geometric, consistency and context-related variables) were constant and such to have a minimum length significant for the accident expectation. All the basic data were obtained from low cost surveys purposely set up. In particular, differential cinematic GPS surveys were used to perform horizontal alignments and Road Safety Inspections (RSIs) were conducted in order to quantify other safety concerns. Based on the identified highway characteristics, different APMs were defined using the Generalized Linear Model approach (GLIM). Considering significant parameters related to exposure, geometric, consistency and context factors as explanatory variables, four APMs with significant goodness-of-fit were selected. Model 1, which considers only exposure, was used to define the length of the homogeneous section in order to obtain a minimum number of expected accidents. Model 2 represents the best statistical fit. Model 3 was defined to avoid illogical results. Finally, even if Model 4 does not represent the best fit of the data, it has the best engineering value as Safety Performance Indicator because at least one significant parameter related to exposure, geometric, consistency and context factors is included in the model.
Abstract