## 5 Modeling Results and Discussion
### Full Bayesian Estimation
If the autocorrelation value converged to zero soon after the iteration, the IID characteristic could meet the requirements. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**MN-Logit Model**} & \multicolumn{4}{c}{**RI-Logit Model**} \\ \cline{2-9}
**Variables** & **PA** & **MO** & **PI** & **TR** & **PA** & **MO** & **PI** & **TR** \\ \hline Male & 0.320 \({}^{\ast\ast}\) & -0.150 \({}^{\ast\ast}\) & 0.160 \({}^{\ast\ast}\) & - & 0.311 \({}^{\ast\ast}\) & -0.139 \({}^{\ast\ast}\) & 0.142 \({}^{\ast\ast}\) & - \\ No\({}^{-}\)clear & -0.435 \({}^{\ast\ast}\) & 0.305 \({}^{\ast\ast}\) & -0.284 \({}^{\ast\ast}\) & -0.250 \({}^{\ast\ast}\) & -0.442 \({}^{\ast\ast}\) & 0.317 \({}^{\ast\ast}\) & -0.253 \({}^{\ast\ast}\) & -0.241 \({}^{\ast\ast}\) \\ Controlled & 0.174 \({}^{\ast\ast}\) & – & – & – & 0.127 \({}^{\ast\ast}\) & – & – & – \\ \(\mathrm{Age\times 60}\) & 0.334 \({}^{\ast\ast}\) & 0.376 \({}^{\ast\ast\ast}\) & 0.488 \({}^{\ast\ast\ast}\) & – & 0.342 \({}^{\ast\ast}\) & 0.351 \({}^{\ast\ast\ast}\) & 0.504 \({}^{\ast\ast}\) & – \\ Drug Kitving & 0.345 \({}^{\ast\ast\ast}\) & 0.161 \({}^{\ast\ast\ast}\) & 1.349 \({}^{\ast\ast}\) & – & 0.339 \({}^{\ast\ast}\) & 0.159 \({}^{\ast\ast}\) & 1.296 \({}^{\ast\ast}\) & – \\ Weekend & – & 0.087 \({}^{\ast\ast}\) & 0.154 \({}^{\ast\ast}\) & 0.710 \({}^{\ast\ast}\) & 0.235 \({}^{\ast\ast\ast}\) & 0.101 \({}^{\ast\ast}\) & 0.147 \({}^{\ast\ast}\) & 0.716 \({}^{\ast\ast}\) \\ Early in month & – & – & 0.161 \({}^{\ast\ast}\) & -0.127 \({}^{\ast\ast}\) & – & – & 0.157 \({}^{\ast\ast}\) & -0.178 \({}^{\ast\ast}\) \\ Late in month & – & – & - & 0.157 \({}^{\ast\ast}\) & – & – & – & 0.161 \({}^{\ast\ast}\) \\ Fall & – & -0.071 \({}^{\ast\ast}\) & – & -0.246 \({}^{\ast\ast}\) & – & -0.083 \({}^{\ast\ast}\) & – & -0.299 \({}^{\ast\ast}\) \\ Winter & 0.376 \({}^{\ast\ast}\) & 0.144 \({}^{\ast\ast}\) & 0.198 \({}^{\ast\ast}\) & – & 0.395 \({}^{\ast\ast}\) & 0.160 \({}^{\ast\ast\ast}\) & 0.202 \({}^{\ast\ast}\) & – \\ Dark (with street lighting) & – & 0.327 \({}^{\ast\ast\ast}\) & – & – & – & 0.355 \({}^{\ast\ast}\) & – & – \\ Dark (without street lighting) & 0.831 \({}^{\ast\ast}\) & 0.349 \({}^{\ast\ast}\) & 0.708 \({}^{\ast\ast}\) & 0.718 \({}^{\ast\ast\ast}\) & 0.368 \({}^{\ast\ast}\) & 0.352 \({}^{\ast\ast\ast}\) & 0.764 \({}^{\ast\ast}\) & 0.737 \({}^{\ast\ast}\) \\ Collision with fixed object & 1.231 \({}^{\ast\ast\ast}\) & 1.105 \({}^{\ast\ast\ast}\) & 1.315 \({}^{\ast\ast}\) & 0.498 \({}^{\ast\ast}\) & 1.248 \({}^{\ast\ast\ast}\) & 1.124 \({}^{\ast\ast\ast}\) & 1.307 \({}^{\ast\ast}\) & 0.510 \({}^{\ast\ast}\) \\ Collision with pedestrian & \(-\)2.126 \({}^{\ast\ast\ast}\) & -2.401 \({}^{\ast\ast}\) & -1.125 \({}^{\ast\ast}\) & -1.105 \({}^{\ast\ast}\) & -2.092 \({}^{\ast\ast}\) & -2.437 \({}^{\ast\ast}\) & -1.164 \({}^{\ast\ast}\) \\ Intercept & \(-\)2.441 \({}^{\ast\ast}\) & 0.698 \({}^{\ast\ast}\) & -2.223 \({}^{\ast\ast\ast}\) & -1.386 \({}^{\ast\ast\ast}\) & -2.608 \({}^{\ast\ast\ast}\) & 0.762 \({}^{\ast\ast}\) & -2.494 \({}^{\ast\ast}\) & -1.327 \({}^{\ast\ast}\) \\ _s.d. intercept_ & – & – & – & 0.632 \({}^{\ast\ast}\) & _0.455_ & _0.318_ & _0.269_ \\ \hline \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%; \({}^{\ast\ast\ast}\) indicates that the risk factor is insignificant or is included in the model.  
\end{table} 
Table 4: Estimation of the parameters of the MN-logit model and the RI-logit model.