## 4 Results
### Parameter Estimation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Urban Roadway**} & \multicolumn{2}{c}{**Distribution of**} \\ \cline{2-5}
**Variable** & **FPL** & **RPLMV** & **Random** \\ \cline{2-5}
**Coefficient** & **t-Stat** & **Coefficient** & **t-Stat** & **Parameter** \\ \hline \multicolumn{5}{c}{Unobserved heterogeneity in means} \\ \hline RAINING: HELMET & 1.105 ** & 2.57 \\ \hline RAINING: NIGHT\_LIGHT & \(-\)1.294 ** & \(-\)1.98 \\ \hline MORNING: ILEGAL\_OVERTAKING & 1.170 ** & 1.96 \\ \hline MORNING: NIGHT\_LIGHT & 1.592 ** & 2.31 \\ \hline \multicolumn{5}{c}{Unobserved heterogeneity in variances} \\ \hline CURVE: VIOLATION & 0.914 * & 1.7 \\ \hline MORNING: VIOLATION & \(-\)35.802 ** & \(-\)79.83 \\ \hline \multicolumn{5}{c}{Model statics} \\ \hline LL(\(\beta\)) & \(-\)4025.653 & \(-\)4006.864 \\ \hline LL(0) & \(-\)4483.470 & \(-\)4483.470 \\ \hline McFadden \(\rho^{2}\) & 0.1021 & 0.1063 \\ \hline \multicolumn{5}{c}{Model comparison via likelihood ratio test} \\ \hline Degrees of Freedom & 9 \\ \hline \(\chi^{2}=-2(\text{LL}(\beta)_{\text{model A}}-\text{LL}(\beta)_{\text{model B}})\) & 37.578 \\ \hline Confidence level & 99.99\% \\ \hline Superior model & RPLMV \\ \hline \multicolumn{5}{c}{Note: FPL denotes fixed parameter logit model, RPLMV denotes the random parameter logit model with unobserved heterogeneity in means and variances. LL (0) denotes the Log-likelihood of the convergence model, LL(\(\beta\)) log-likelihood of the model with parameter estimation. ** \(p\)-value < 0.05, * \(p\)-value < 0.1.} \\ \multicolumn{5}{c}{**5. Discussion**} \\ \multicolumn{5}{c}{Table 7 and Figure 5 display the Marginal Effect (ME) used to compare the severity of crashes involving Young Adult Motorcyclists (YAM) on urban and rural local roads.} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Marginal effects. Gender analysis of YAMs revealed that males were more likely to die than females in both road areas, a finding consistent with numerous studies. [PERSON] et al. [11] argued that males exhibited more aggressive driving behaviors than females, especially among YAMs. Considering the ME loadings, the urban roads showed higher values. Male YAMs were more likely to die in urban crashes than in rural crashes (ME Urban = 0.0708 (t-stat = 7.66), ME Rural = 0.0558 (t-stat = 8.74)). In the present study, we present a demographic analysis of driver's licenses categorized by gender as of 31 May 2020. The driver's licenses under scrutiny encompass three distinct categories for motorcycle operation, motorcycle (temporary), motorcycle (five years), and motorcycle (life). The data reveals that the male population possesses a total of 8,691,794 licenses, while the female population holds 4,547,454 licenses within the specified categories [33]. The variable representing crashes with foreign drivers was not significant in either model, though it emerged as a random parameter (Figure 6a). YAM crashes with at-fault riders were less likely to result in fatalities (ME Urban = \(-\)0.0405 (t-stat = 3.98), ME Rural = \(-\)0.0499 (t-stat = 6.86)). At-fault riders in this context also refer to single crashes, which typically do not involve different-sized vehicles, leading to a lower chance of death. [PERSON] [20] confirmed this effect, finding that motorcycle crashes with other vehicle types caused more fatalities than single crashes. Supplementary data pertaining to the aggregate count of registered vehicles in Thailand as of 31 December 2022, elucidate that the distribution of these vehicles consists of 22,137,636 motorcycles, accounting for 52.66% of the total, followed by 11,334,873 passenger cars designed to accommodate no more than seven passengers, representing 26.99% of the total, and 7,085,910 personal cars, constituting 16.86% of the overall vehicular population [34]. Local drivers were less likely to die in both urban and rural road crashes (ME Urban = \(-\)0.0362, ME Rural = \(-\)0.0254).