Abstract
The object of this research is to develop one and only injury crash rate prediction model differentiable for three main crash types (head-on/side collisions, rear-end collisions, single-vehicle run-off-road crashes) observed on the selected Italian two-lane rural roads in low-volume conditions. An explanatory variable reflecting road “Surface” conditions (dry/wet), “Light” conditions (day/night), and geometric “Element” (tangent segment/circular curve) when the crash happened and referred to the police reports has been proposed within the safety performance function all together (Surface, Light and Element) with three other significant variables (lane width, horizontal curvature indicator and mean speed) as consistent factors to predict crashes and their degree of seriousness for different kind of crashes. Among different statistical approaches introduced in the past few years to deal with the data and methodological issues associated with crash-frequency data, a generalized estimating equation has been implemented to take into account over-dispersion of the crash data, with a negative binomial distribution additional log linkage equation. Residual plots were combined with the validation procedure and other goodness-of-fit measurements to determine the reliability of the results. Potential countermeasures have been proposed for the critical crash types surveyed on the studied roads; these countermeasures have had positive effects on the road segments where the serious crash types have occurred over an eight-year period of analysis.
Highlights
Low-volume roads (LVRs), as presented in this paper, are characterized as roads with fewer than 1000 vehicles per day for most times of the year, the definition can differ
The objective of the study presented here is to develop an injury crash rate prediction model that is differentiable for three specific crash types identified on the studied two-lane rural roads in low-volume conditions
The crash data used in this research involved almost 600 km of two-lane rural roads in Southern Italy located in the flat area with a vertical grade of less than 6%; one half were used for the calibration procedure of the safety performance function, attempting to predict the number of injury crashes for year for km for 108 vehicles and the other half to check the effectiveness of the crash prediction model in the validation procedure
Summary
Low-volume roads (LVRs), as presented in this paper, are characterized as roads with fewer than 1000 vehicles per day (vpd) for most times of the year, the definition can differ. The objective of the study presented here is to develop an injury crash rate prediction model that is differentiable for three specific crash types identified on the studied two-lane rural roads in low-volume conditions (head-on/side collision, rear-end collision, single-vehicle run-off-road crash). The research illustrated here follows a “network” approach for the safety analysis of the investigated road segments: a) identification of the injury critic crash type on the studied road network with highest frequency of occurrence on the roadway segments during the analysis period; b) identification of “black” roadway segments for a specific crash type, by using an injury crash rate prediction model, where the crash injury rate is higher than on the rest of the roads; c) identification of accurate and precise countermeasures for the crash dynamics These steps will be integrated in the future developments with the following future advances; d) assessment of the difference between the after and before proportions of the injury crash rates at each treatment site for a specific target collision type; e) assessment of the average difference between after and before proportions over all n treatment sites; f) assessment of the statistical significance of the average shift in proportion of the target collision type. The experimental analysis was divided into two phases: phase I − the calibration phase involving 300 km of analyzed two-lane rural roads and phase II – the validation phase involving an additional 300 km not included in the first step on which to test the effectiveness of the model
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