Nationally, transportation agencies have embarked on efforts to collect information digitally on highway attributes to help understand factors that contribute to traffic crash occurrences. Instrumented vehicles, database modeling efforts, and enhancements in crash-data collection are salient examples of such efforts. This paper provides insights into the prospective value of roadway information as it pertains to statistical analysis of severity of crashes. It presents a case study analysis from Washington State that involves divided highway crash data. A statistical model is presented that demonstrates an empirical relationship between key roadway variables and distributions of crash severity. The other notable output of this paper involves the contribution of weather information to the distributions of crash severity. While the case study is restricted to divided highways in the northwest part of the United States, the statistical insights from the analysis of severity distributions indicate the prospective value of key data elements in relation to their regular measurement and updates to statewide crash risk management.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Mechanical Engineering