Best Practices for Road Weather Management Version 2.0
Spatial Analysis of Weather Crash Patterns in Wisconsin
A case has been presented in this paper for the use of spatial statistical techniques in analyzing crash data to investigate spatial patterns and spatial autocorrelation. "Weather-related" crashes in Wisconsin, aggregated on a county level, have been analyzed using spatial statistical techniques. The Getis-Ord Gi statistic was used to identify spatial patters of different types of weather-related crashes. The statistic shows that the spatial patterns for weather-related are clustered at different locations in the state depending upon the weather conditions (snow, rain, fog, and ice). The results also show counties of statistically significant high and low relative crash rates (number of a particular weather-related crash as a percentage of total number of crashes) for each weather condition. These spatial patterns validate the fact that weather impacts crash frequencies and rates for these counties. Furthermore, the results for snow crashes have been validated by comparing counties of high and low snow crash rates with areas of high and low winter severity index values. The results show that counties of high snow crash rates lie within areas of more severe winter and vice versa. The establishment of this relationship between weather and crashes paves the way for the next step of scrutinizing these patterns to identify the variables contributing to these crashes and the implementation of effective countermeasures for Road Weather Safety Audit purposes.
85th Transportation Research Board (TRB) Annual Meeting, University of Wisconsin-Madison
Khan, Qin, Noyce
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