Secondary Low Volume Rural Road Safety: Segmentation, Crash Prediction, and Identification of High Crash Locations

Abstract

Traffic safety research is important to understand the interactions and relationships between crashes and the roadway. Methods have been established for segmenting roadways for safety analysis, creating safety performance functions, and identifying high crash locations. However, little work or reasoning is available to provide guidance for segmenting and modeling secondary low volume rural roads (LVRRs). This study investigated the effect of secondary LVRR segment length on segment analysis. Safety performance models were also examined and created for secondary LVRRs. Using previously proposed tests, four different high crash identification methods (crash frequency, crash rate, empirical Bayes and crash reduction potential) were compared for use on secondary LVRRs in Iowa. Analysis of the secondary LVRR system identifies a trend showing as segment length increases, so does the statistical reliability of the average annual crash frequency as compared to the variance in crash frequencies from year to year. Serious and total crash prediction models are recommended for use on four different classes of mainline secondary LVRRs: paved and unpaved 1-99 AADT, and paved and unpaved 100-400 AADT. Lastly, empirical Bayes is recommended as the best available method for identifying high crash locations on secondary LVRRs in Iowa. Care is advised when developing candidate high crash location lists for secondary LVRRs based on segmented systems where systemic treatment may be more appropriate.