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- W37538320 abstract "Traffic safety during winter seasons has been a serious concern for transportation agencies and road users in state with severe winters. Given the limited resources available to state agencies for winter weather operations, it is necessary to establish a prioritization technique for identifying high crash risk locations during winter conditions. Traditional methods to identify crash hot spots suffer from serious limitations including the regression-to-mean problem. These methods also typically do not consider weather information for identifying hot spots for safety countermeasure action. This paper proposes a novel approach to extract weather data from information reported by winter maintenance crew members. This approach is applied to incorporate weather-related factors (such as visibility, wind velocity, air temperature) in developing winter-weather crash frequency models at the network level for three roadway types in Iowa. Then, this paper employs the Empirical Bayes technique to rank study roadway segments based on their potential for safety improvement. The developed winter weather crash frequency models can be used to make data driven decisions to prioritize road segments for improving winter weather operations and safety." @default.
- W37538320 created "2016-06-24" @default.
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- W37538320 date "2015-01-01" @default.
- W37538320 modified "2023-09-24" @default.
- W37538320 title "An Application of the Empirical Bayes Method for Identifying Winter Weather Crash Hot Spots" @default.
- W37538320 hasPublicationYear "2015" @default.
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