## 4 Results
### Parameter Estimation Results
This may be attributed to fewer vehicles on rural roads, encouraging drivers to drive faster, and weaker or virtually nonexistent speed law enforcement, which increases young adult males' confidence in their speed use [38]. Regarding the crash model for urban roads, EXCESS_SPEED_LIMIT is identified as a random parameter, implying that speeding accounts for approximately 32.1% of non-fatal crashes, as shown in Figure 6c. Unobserved heterogeneity in the mean reveals a negative parameter for RAINING, which suggests that crashes occurring during rainy weather lower the mean of EXCESS_SPEED_LIMIT. In other words, while a YAM crash may be caused by exceeding the speed limit, the likelihood of fatality decreases if the crash happens when it is raining. Similarly, the unobserved heterogeneity in variance shows that crashes on wet road surfaces reduce the variance of the scale of random parameter EXCESS_SPEED_LIMIT. Other factors, such as traffic signal violations and illegal overtaking, are not significant in the model. Mobile phone use while driving is only significant in rural road models. The sudden cut in front is significant in both models (ME Urban = \(-\)0.0933 (t-stat = 8.67), ME Rural = \(-\)0.0456 (t-stat = 5.49)), resulting in a lower chance of fatality. The road factor was only significant in the rural crash model. Crashes occurring on curved roads or roads with damaged surfaces were found to have a lower likelihood of resulting in fatalities. A plausible explanation for this is that YAMs tend to adjust their driving behavior, slowing down when approaching curves or damaged road surfaces. However, the curve variable was identified as a random parameter in Figure 7a (58.2% below zero), suggesting that around 40.8% of crashes on curved roads lead to high fatality rates. This type of crash may be the result of a group of individuals driving at such high speeds that they cannot reduce their speed in time. The variable exhibits unobserved heterogeneity in variance, CURVE: VIOLATION, which indicates that traffic sign violations increase the variance of the random parameter of CURVE. Crashes during rainy weather are only significant on rural roads and were identified as a random parameter, with a mean ME of \(-\)0.1626, suggesting that such crashes result in fewer fatalities. The distribution is 76.3% below zero (Figure 7b). This finding is reasonable since motorcycles often cannot continue driving in rainy conditions. [PERSON] et al. [39] also noted another behavior increased cautiousness and slower driving in rain. Although the risk of a crash may be higher in wet conditions, the severity tends to be lower compared to driving in dry conditions. Unobserved heterogeneity in the means of this variable includes: (1) RAINING: HELMET, a positive parameter that indicates an increased mean value, and (2) RAINING: NIGHT_LIGHT, a negative parameter that signifies a decreased mean value. Figure 7: Distribution of random parameters in the rural roadway model. (**a**) CURVE; (**b**) RAINING; and (**c**) MORNING. SMOKE_DUST_FOG were significant in both models, with the potential to reduce fatality rates, while wet crashes were not significant in either model. The crash time variable, which was found to be significant during both morning and afternoon in both models for crashes occurring in the same direction, is associated with a lower likelihood of fatalities. This suggests that driving in sufficient light reduces the severity of crashes. A possible reason is that YAMs can see more clearly and reduce their speed before a crash occurs. However, the MORNING variable for rural crashes was identified as a random parameter with a distribution of 70.0% below zero, as shown in Figure 7c. In these cases, 30% of crashes have a high probability of fatality. As for the unobserved heterogeneity in the mean, MORNING: ILEGAL_OVERTAKING showed a positive direction, indicating an increase in the mean, while MORNING: NIGHT_LIGHT implies that the likelihood of death decreases during daylight incidents.