Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models

Two-lane, two-way roads constitute a major portion of the rural roads in most countries of the world. This study identifies the factors influencing crash injury severity on these roads in Iran. Classification and regression trees (CART), which is one of the most common methods of data mining, was employed to analyze the traffic crash data of the main two-lane, two-way rural roads of Iran over a 3-year period (2006–2008). In the analysis procedure, the problem of three-class prediction was decomposed into a set of binary prediction models, which resulted in a higher overall accuracy of the predictions of the model. In addition, the prediction accuracy of the fatality class, which was nearly 0% in some of the previous studies, increased significantly. The results indicated that improper overtaking and not using a seatbelt are the most important factors affecting the severity of injuries.

► We used 3-year period (2006–2008) crash data of Iran two-lane, two-way rural roads.
► Classification and regression trees (CART) was employed to analyze the traffic crash data.
► Increasing prediction accuracy of the fatality class by decomposing three-class into a set of binary prediction models.
► Improper overtaking and not using a seat belt are the most important factors affecting the severity of injuries on two-lane, two-way rural roads.