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Abstract
The objective of this research was to develop a methodology for targeted pavement friction data collection based on the analysis of weather-related crashes. Furthermore, the aim was to identify threshold values of pavement friction characteristics indicating a significant impact on safety prompting the need for maintenance and improvements. Spatial analysis using Local Moran’s I statistic identified hotspots where pavement friction data were collected. A master database was assembled including Wisconsin State Trunk Network (STN) road attributes, hotspots of weather-related crashes, and pavement friction data collected based on hotspot analysis. The analysis results provide evidence in support of hotspot analysis as a viable procedure for targeted pavement friction data collection to enable efficiency and cost reductions. Classification tree analysis using GUIDE (Generalized, Unbiased, Interaction Detection and Estimation) algorithm was used to further explore the relationship between pavement friction characteristics and safety. Statistically significant hotspots were observed below a pavement friction number of approximately 57 and very high hotspots below a pavement friction number of approximately 42. The results indicate that pavement friction thresholds identified in the literature between 20 and 32 may be too low and that safety may be impacted at friction numbers as high as in the forties. The results also show differences in friction and safety for various types of pavement surfaces. The use of weather-related crashes provides a data-driven and cost-effective method of prioritizing locations for pavement friction data collection and maintenance. Results from this research can be readily used in initial steps of systemic road safety management procedures by practitioners.