Examination of Methods to Estimate Crash Counts by Collision Type

Abstract

Multinomial logit (MNL) models have been applied extensively in transportation engineering, marketing, and recreational demand modeling. Thus far, this type of model has not been used to estimate the proportion of crashes by collision type. This study investigated the applicability of MNL models to predict the proportion of crashes by collision type and to estimate crash counts by collision type. MNL models were compared with two other methods described in recent publications to estimate crash counts by collision type: (a) fixed proportions of crash counts for all collision types and (b) collision type models. This study employed data collected between 2002 and 2006 on crashes that occurred on rural, two-lane, undivided highway segments in Minnesota. The study results showed that the MNL model could be used to predict the proportion of crashes by collision type, at least for the data set used. Furthermore, the method based on the MNL model was found useful to estimate crash counts by collision type, and it performed better than the method based on the use of fixed proportions. The use of collision type models, however, was still found to be the best way to estimate crash counts by specific collision type. In cases where collision type models are affected by the small sample size and a low sample-mean problem, the method based on the MNL model is recommended.