Nationally transportation agencies have begun to embark on efforts to digitally collect information on highway attributes to aid in better understanding of factors contributing to traffic crash occurrences. Instrumented vehicles, database modeling efforts and crash data collection enhancements are salient examples of such efforts. In this light, this paper attempts to provide some insights into the prospective value of roadway information as it pertains to statistical analysis of severity of crashes. In doing so, it presents a case study analysis from Washington State involving divided highway crash data. A statistical model is presented demonstrating an empirical relationship between key roadway variables and severity crash distributions. The other salient output from this paper involves the contribution of weather information to severity distribution of crashes. As a final note, while the case study is restrictive to divided highways in the northwest part of the United States – the statistical insights from the analysis of severity distributions indicates prospective value of key data elements in terms of their regular measurements and updates in a statewide crash risk management sense.
Abstract