Identifying Factors Affecting Rural Crash Severity Using Multinomial Logit (MNL) Model (Case Study Ilam Province)

Identifying the factors that contribute to injury as a result of crashes will help policy makers and road designers implement countermeasures, which could reduce crash injury severity, and cost. In result, objective of this study is identifying factors are more likely to contribute to crashes severity. Today, Researchers have utilized mathematical and statistical modeling-schemes to solve this complex road-safety problem. For this, discrete choice modeling is widely used to model injury severity of the crash. From the models, factors contributing to the injury severity are identified. MNL is the most prominent discrete choice model for modeling crash severity. For modeling, the data of a 5-year period of crashes occurred at the rural roads of Ilam province has been used. The input variables of the model were selected after a significance evaluation test. These variables included Age, Speed, Alcohol, Head-on, Airbag, Ejection, Seatbelt, following too Close, Gender, and Curved. After selecting the variables, the accuracy of the model was also studied. To validate the model, the likelihood ratio and the percent correctly predicted by the model at each crash severity level were used. After evaluating the model accuracy in the prediction of accidents severity, it was found that the model provides acceptable results for evaluating crashes severity, and it was found that except curve and following too close variables, all selected variables contributing in crashes severity.