Traffic accidents cause life and money loss. Proper identification of accident casual factors is essential for composing countermeasures against traffic accidents and reducing related costs. However, two-lane rural roads have distinct roadway characteristics compared with other types of roads. In order to find cost-effective countermeasures and prioritize roadway safety improvement plans, a better understanding of the relationship between accident risk and respective characteristics is necessary. This study focuses on analyzing accident analysis on two-lane rural road sections in Washington State. Six representative state routes (SRs), SR-2, SR-12, SR-20, SR-21, SR-97 and SR-101, are selected as study routes based on their location, length, accident history, and geometric characteristics,. Along with six-year (1999~2004) accident data from Highway Safety Information System (HSIS), roadway video image data and geographical information system data retrieved from Washington State Department of Transportation are employed in this study. Econometric modeling methods are utilized to identify accident causal factors and evaluate their impacts on accident risk. Poisson regression, negative binomial regression, and zero-inflated regression models are evaluated and negative binomial regression is found to be the most suitable form for modeling accidents on two-lane rural roads. In addition to modeling all-type accidents, a rear-end accident risk model is also developed since rear-end accidents are the most frequent accident type on all routes. Findings from this study not only help identify accident causal factors, but also provide valuable insights for developing countermeasures against two-lane rural road traffic accidents.
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