## 5 Modeling Results and Discussion
### Full Bayesian Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**RP-Logit Model**} & \multicolumn{4}{c}{**SRP-Logit Model**} \\ \cline{2-9}
**Variables** & **PA** & **MO** & **PI** & **TR** & **PA** & **MO** & **PI** & **TR** \\ \hline Male & 0.317 \({}^{\ast\ast}\) & \(-\)0.144 \({}^{\ast\ast\ast}\) & 0.157 \({}^{\ast}\) & – & 0.315 \({}^{\ast\ast}\) & \(-\)0.147 \({}^{\ast\ast\ast}\) & 0.152 \({}^{\ast}\) & – \\ _s.d. male_ & _1.218_ & _0.497_ & _0.037_ & – & 1.197 & 0.503 & 1.043 & – \\ Non-clear & \(-\)0.447 \({}^{\ast}\) & 0.312 \({}^{\ast}\) & \(-\)0.259 \({}^{\ast}\) & \(-\)0.236 \({}^{\ast}\) & \(-\)0.439 \({}^{\ast\ast}\) & \(-\)0.342 \({}^{\ast\ast\ast}\) & \(-\)0.261 \({}^{\ast}\) & \(-\)0.229 \({}^{\ast}\) \\ _s.d. non-clear_ & 0.134 \({}^{\ast\ast}\) & – & 0.107 & _0.513_ & 0.140 & – & – \\ Controlled & 0.181 \({}^{\ast\ast}\) & – & – & – & – & – & – \\ Age \(>\) 60 & 0.340 \({}^{\ast\ast\ast}\) & 0.349 \({}^{\ast\ast\ast}\) & 0.494 \({}^{\ast\ast}\) & – & 0.357 \({}^{\ast\ast\ast}\) & 0.346 \({}^{\ast\ast\ast}\) & 0.473 \({}^{\ast\ast\ast}\) & – \\ Drunk driving & 0.340 \({}^{\ast\ast\ast}\) & 0.166 \({}^{\ast\ast\ast}\) & 1.251 \({}^{\ast\ast\ast}\) & – & 0.343 \({}^{\ast\ast\ast}\) & 0.163 \({}^{\ast\ast\ast}\) & 1.248 \({}^{\ast\ast\ast}\) & – \\ s.d. _d. dimu driving_ & – & _1.532_ & – & – & – & 1.408 \({}^{\ast\ast}\) & – \\ Weeked & 0.237 \({}^{\ast\ast}\) & 0.105 \({}^{\ast}\) & 0.165 \({}^{\ast}\) & 0.712 \({}^{\ast}\) & 0.222 \({}^{\ast\ast\ast}\) & 0.002 \({}^{\ast\ast}\) & 0.151 \({}^{\ast\ast}\) & 0.707 \({}^{\ast\ast}\) \\ _s.d. neutral_ & – & 0.371 \({}^{\ast}\) & – & 1.030 \({}^{\ast}\) & – & 0.401 \({}^{\ast}\) & – & 1.206 \({}^{\ast}\) \\ Early in month & – & – & 0.167 \({}^{\ast}\) & \(-\)0.169 \({}^{\ast\ast}\) & – & – & 0.165 \({}^{\ast}\) & – \\ Late in month & – & – & \(-\)0.168 \({}^{\ast}\) & – & – & – & 0.165 \({}^{\ast}\) & – \\ Fall & – & \(-\)0.066 \({}^{\ast}\) & – & \(-\)0.232 \({}^{\ast}\) & – & \(-\)0.087 \({}^{\ast\ast}\) & – & – \\ Drunk Winter & 0.303 \({}^{\ast\ast\ast}\) & 0.168 \({}^{\ast\ast}\) & 0.227 \({}^{\ast}\) & – & 0.389 \({}^{\ast\ast\ast}\) & 0.157 \({}^{\ast\ast}\) & 0.219 \({}^{\ast}\) & – \\ Dark(without street lighting) & 0.847 \({}^{\ast\ast}\) & 0.341 \({}^{\ast\ast\ast}\) & 0.693 \({}^{\ast}\) & 0.769 \({}^{\ast\ast}\) & 0.845 \({}^{\ast\ast}\) & 0.347 \({}^{\ast\ast\ast}\) & 0.725 \({}^{\ast\ast}\) & 0.771 \({}^{\ast}\) \\ Collision with fixed object & 1.257 \({}^{\ast\ast\ast}\) & 1.151 \({}^{\ast\ast}\) & 1.240 \({}^{\ast\ast}\) & 0.509 \({}^{\ast\ast}\) & 1.268 \({}^{\ast\ast}\) & 1.147 \({}^{\ast\ast\ast}\) & 1.251 \({}^{\ast\ast}\) & 0.512 \({}^{\ast\ast}\) \\ Collision with pedestrian & \(-\)2.125 \({}^{\ast\ast\ast}\) & \(-\)2.409 \({}^{\ast\ast}\) & \(-\)1.094 \({}^{\ast\ast}\) & \(-\)1.065 \({}^{\ast\ast\ast}\) & \(-\)2.157 \({}^{\ast\ast\ast}\) & \(-\)2.416 \({}^{\ast\ast\ast}\) & \(-\)1.119 \({}^{\ast\ast\ast}\) & \(-\)1.061 \({}^{\ast\ast}\) \\ Intercept & \(-\)2.637 \({}^{\ast\ast}\) & 0.519 \({}^{\ast}\) & \(-\)2.179 \({}^{\ast\ast}\) & \(-\)1.207 \({}^{\ast\ast}\) & \(-\)2.900 \({}^{\ast\ast\ast}\) & 0.507 \({}^{\ast\ast\ast}\) & -2.165 \({}^{\ast\ast\ast}\) & \(-\)1.238 \({}^{\ast\ast\ast}\) \\ \({}^{\ast\ast}\) & – & – & – & 0.712 \({}^{\ast\ast}\) & – & 0.723 \({}^{\ast\ast\ast}\) & 0.955 \({}^{\ast\ast}\) & 0.752 \({}^{\ast\ast}\) & -1.238 \({}^{\ast\ast\ast}\) \\ \({}^{\ast\ast}\) & – & – & – & 0.543 \({}^{\ast\ast}\) & 0.691 \({}^{\ast\ast}\) & – & 0.749 \({}^{\ast\ast}\) & 0.749 \({}^{\ast\ast}\) \\ \hline \end{tabular} Note: \({}^{\ast\ast}\) indicates 90% credit interval: \({}^{\ast\ast}\) indicates 95% credit interval: \({}^{\ast\ast\ast}\) indicates a credit interval above 95% \({}^{\ast\ast}\) indicates that the risk factor is insignificant or is not included in the model.  
\end{table} 
Table 8: Estimation of the parameters of the RP-logit model and the SRP-logit model.