Next, Another study identified the personal and

Next, we fit alogistic regression model to our data set. Logistic regression is a statistical method foranalyzing a dataset in which there are one or more independent variables thatdetermine an outcome. The outcome is measured with a dichotomous variable (inwhich there are only two possible outcomes). There have been some past studies in thehighway safety area in which logistic regression models had been used toidentify various influential factors. In one such study in Iowa, age and genderwere investigated as predictors of injury severity in head-on highway crashes byusing logistic regression 41. Another study identified the personal andbehavioral predictors of automobile crash and injury severity in Hawaii 42.

Another study, also in Hawaii, used logistic regression modeling to identifydemographic and temporal factors associated with impaired motorcycle crashes43. It has also been used to examine the predictors of safety belt use amongcrash-involved drivers and front seat passengers while allowing formisclassification errors in the outcome variable 44. Pennsylvania has alsoused logistic regression modeling to estimate the influences of driver,highway, and environmental factors on run-off-road crashes 45.We split the data to validate the model into train data and testdata. Train data consists of 1500 data and test data consists of 568 data.

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Thethreshold value is 0.5. If probability > 0.5, we consider the accident willbe reported and if the probability < 0.

5, the accident will not be reported.Based on that, we estimate the proportion of underreported accident. A logistic regression model was fitted intoaccident data set consisting of a previously specified set of fifteendichotomous predictors: age, gender, ethics, religions, residential locations,residential, status, type of road users, vehicle damaged, injury, death, thepresence of objects, type of road, hospitalized and location of the accident46.

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