## 4 Results
### Parameter Estimation Results
It is clear that familiarity with routes allows for caution at dangerous points and an understanding of local behaviors [35]. LOCAL_ADDRESS emerged as a random parameter (Figure 6b) (55.2% below zero), indicating a 44.8% chance that local people had a higher probability of death. This variable exhibited unobserved heterogeneity in variances, specifically LOCAL_ADDRESS: WET. The parameter value was negative, suggesting that wet road surfaces can reduce the crash variance for local YAMs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Urban Roadway**} & \multicolumn{3}{c}{**Rural Roadway**} \\ \hline
**Variable** & **Effect** & **t-Stat** & **95\% CI** & **Effect** & **t-Stat** & **95\% CI** \\ \hline CUTTING\_FRONT & \(-\)0.0933 & 8.67 & \(-\)0.1143 & \(-\)0.0722 & \(-\)0.0456 & 5.49 & \(-\)0.0618 & \(-\)0.0293 \\ \hline CURVE & & & & & **\(-\)0.0315** & 3.20 & \(-\)0.0508 & \(-\)0.0122 \\ \hline ROUGH & & & & & \(-\)0.0335 & 2.08 & \(-\)0.0650 & \(-\)0.0019 \\ \hline RAINING & & & & & **\(-\)0.1626** & 20.45 & \(-\)0.1782 & \(-\)0.1470 \\ \hline SMOKE\_DUST\_FOG} & \(-\)0.1120 & 5.05 & \(-\)0.1555 & \(-\)0.0685 & \(-\)0.0385 & 2.82 & \(-\)0.0653 & \(-\)0.0117 \\ \hline WET & & & & & & & \\ \hline AFTERNOON & \(-\)0.0739 & 6.41 & \(-\)0.0965 & \(-\)0.0513 & \(-\)0.0362 & 5.14 & \(-\)0.0500 & \(-\)0.0224 \\ \hline MORNING & \(-\)0.0587 & 4.49 & \(-\)0.0843 & \(-\)0.0331 & \(-\)**0.0883** & 8.67 & \(-\)0.1083 & \(-\)0.0684 \\ \hline NIGHT\_LIGHT & 0.0231 & 1.97 & 0.0001 & 0.0460 & & & & \\ \hline DARK & 0.0617 & 3.87 & 0.0305 & 0.0930 & 0.0376 & 5.00 & 0.0229 & 0.0524 \\ \hline \end{tabular} Note: Bold text denotes the random parameter.  
\end{table} 
Table 7: _Cont._Wearing a helmet was not significant in the model, which is intriguing given the findings of previous research. In most cases, wearing a helmet reduces the chances of death for motorcyclists [36]. The insignificance of helmet-wearing could be interpreted as not significantly reducing the likelihood of death in YAM crashes. One possible reason is that many YAMs use half helmets, which have a higher chance of resulting in death compared to full helmets. [PERSON] et al. [5] found that the use of a standard helmet was significantly more effective in reducing the likelihood of death. YAMs who crashed while drunk had a lower chance of death, which is not entirely consistent with [PERSON] et al. [15], who found that drunk driving is a significant risk factor for severe motorcycle crash injuries in university neighborhoods. However, similar to [PERSON] et al. [6], the reason may be that, even though the YAMs rode fast while drunk, their physical strength resulted in injuries but not death. In urban areas, the parameters were more negative than on rural roads, indicating a lower chance of death, which could be due to the greater density of rescue points, allowing for quicker access to victims. Unlicensed YAMs had a higher probability of death (ME Urban = 0.1475 (t-stat = 11.8), ME Rural = 0.0823 (t-stat = 7.56)), with crashes in urban areas almost twice as likely to be fatal as those in rural areas. Similar to [PERSON] and [PERSON] [37], this may be attributed to the greater variety of cars on urban roads compared to rural roads,  
Figure 5: Marginal Effects. Figure 6: Distribution of random parameters in the urban roadway model. (**a**) FOREIGN; (**b**) LOCAL_ADDRESS; and (**c**) EXCESS_SPEED_LIMIT. leading to crashes with larger vehicles and a lack of skills to avoid crashes, ultimately resulting in more severe crashes. Several key factors contribute to crashes. One significant cause is crashes resulting from exceeding the speed limit in both urban and rural areas, which leads to a higher likelihood of fatal outcomes. The crash model reveals a higher ME value for rural roads (ME Urban = 0.1100 (t-stat = 9.77), ME Rural = 0.1486 (t-stat = 20.52)), indicating that if a crash involving a YAM occurs due to speeding, it is more likely to be fatal in rural settings.