Therefore, this study was set forward to identify the contributing factors of fatal and injury crashes on the FPIR. In this study, the logistic regression model was chosen due to its usefulness in understanding the influence of independent variables on a dichotomous dependent variable (fatal and injury crashes and PDO).
The objective of this study is to determine factors contributing to crash severity in the FPIR. Four binary logistic regression models were developed for all roads within the FPIR and roads under the three jurisdictions Indian Tribe, city and county highway agency, and the state highway agency to study their differences in terms of crash contributing factors that affect crash severity.
Crash data within the FPIR was collected from the MDT.
FPIR is no exception with 24% of the crashes occurring with a driver under the influence of a substance.
For all roads within the FPIR, the p values for the deviance and Pearson chi-square test were 0.37 and 0.77, respectively.
The results obtained for all roadways within the FPIR and individual roadway systems are presented in Table 3.
All Roadways within the FPIR. The first model included all the roadways within the FPIR.
For all roadways within the FPIR, impaired driving, adverse weather condition, and roadway system (Indian reservation roads and state highway) were found to be predominant predictors of fatal and injury crashes.