Abstract
Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Moran's I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.
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