Validation of CMFs Derived from Cross-Sectional Studies Using Regression Models

1 Crash modification factors (CMFs) can be used to capture the safety effects of countermeasures 2 and play significant roles in traffic safety management. As an alternative to the before-after 3 study, the regression model method has been widely used for estimating CMFs. Although before4 after studies are considered to be superior, the use of regression models for estimating CMFs has 5 never been fully investigated. This paper consequently sought to examine the conditions in 6 which regression models could be used for such purpose. CMFs for three variables, lane width, 7 curve density and pavement friction, were assumed and used for generating random crash counts. 8 Then, CMFs were derived from regression models using the simulated crash data for three 9 different scenarios. The results were then compared with the assumed true value. The study 10 results showed that (1) the CMFs derived from the regression models should be unbiased when 11 all factors affecting traffic safety are identical in all segments, except those of interest; (2) if 12 some factors having minor safety effects are omitted from the models, the accuracy of estimated 13 CMFs can still be acceptable; (3) if some factors already known to have significant effects on 14 crash risk are omitted, the CMFs derived from the regression models are generally unreliable. 15 Thus, depending on the missing variables that are not included in the model, the transportation 16 safety analyst can decide if the CMFs developed from the regression models should be used for 17 highway safety applications. 18