Rule-based safety prediction models for rural two-lane run-off-road crashes

Abstract

• Analyzed rural two-lane run-off-road crashes in Louisiana. • Applied both statistical and machine learning models to determine the best-fit model. • Cubist provides the highest accuracies with rules-based regression models. • Rules-based models are capable in addressing localized issues by improving prediction accuracies. During 2015–2017, the yearly average of fatalities caused by roadway departure (RwD) crashes was 19,233 in the U.S. Roadway departure crashes, or crashes in which a vehicle crosses an edge line, centerline, or otherwise leaves the traveled way, account for 52 percent of all traffic fatalities in the U.S. during this time. A majority of RwD crashes are run-off-road (ROR) crashes; these are crashes that result in a vehicle crossing the edge line on either side of the roadway. In this study, the research team analyzed seven years (2010–2016) of rural two-lane ROR crash data from Louisiana to better comprehend ROR crashes in refining safety predictions for rural two-lane highways. Statistical model (negative binomial model) and three separate machine learning models (random forest, support vector machine, and Cubist) were applied to determine the best fit models. Overall, Cubist is characterized by a better performance in estimating ROR crashes on rural two-lane roadways. The Cubist approach introduced rules-based safety performance functions (SPFs) for total and fatal and injury crashes. This approach will be beneficial for the safety practitioners in tackling localized issues in crash data with an emphasis on prediction accuracy.