## 5 Modeling Results and Discussion
### Discussion
[15] and [PERSON] [73] and noted that the physical capabilities of older drivers are not satisfactory, as they require more reaction time in case of an emergency, which result in a greater susceptibility to serious crashes [74]. However, this variable becomes totally insignificant once truck crashes are accommodated. [PERSON] et al. [59] obtained a similar conclusion by analyzing rural SV crashes in China and believed that more driving experience may be a dominant factor in determining crash severity. Hence, government departments may consider providing regular training and driving skills tests for older adults, with the aim of improving their traffic safety performance. The impact of drunk driving on traffic safety cannot be ignored. In passenger car, motorcycle, and pickup crashes, drunk driving will result in a significant increase in the probability of severe rural SV crashes (marginal effects 2.36%, 3.95%, and 4.99%, respectively). This finding is confirmed in the existing literature [75; 76], which points out that if a driver is under the influence of alcohol, it is difficult to maintain a satisfactory  
Figure 4: Average marginal effect of significant risk variables. driving status and is more likely to be involved in serious rural SV crashes. These findings demonstrate the detrimental effects of drunk driving on rural traffic safety to policymakers and the general public. Furthermore, traffic management and road infrastructure in rural China are inadequate, which further increases the probability of serious collisions. The percentage of drunk driving in the whole dataset is as high as 15.33%; hence, the management of drunk driving should be strengthened. Note that drunk driving in the model of truck crashes was artificially deleted because it had only 33 observations (0.6%). The statistical results show that truck drivers have superior safety awareness compared to other motor vehicle types; a similar finding was pointed out by [PERSON] et al. (2017). In addition, the parameter of drunk driving in the model of rural motorcycle SV crashes follows a normal distribution with a mean of 0.163 and a standard deviation of 1.408. However, there is no heterogeneity for this variable in other vehicle types. Hence, by establishing rural SV crash models under different vehicle types, the source of heterogeneity of risk factors in the whole dataset can be clarified. The impact of the weekend on the severity of rural SV crashes is significant, which remained stable across different vehicle types. Weekend driving resulted in a 0.81%, 1.48%, 2.69%, and 1.07% increase in the probability of FS injuries in passenger car, motorcycle, pickup, and truck crashes, respectively. This is generally consistent with the previous studies. For example, [PERSON] et al. (2017) and [PERSON] et al. (2017) analyzed motorcycle crashes and truck crashes, respectively, and both suggested that the probability of a serious crash was significantly higher on weekends than on weekdays. These findings underscore the importance of educating drivers to remain vigilant after long off-duty hours, such as on weekends, and provide theoretical references for formulating scientific management measures. For example, traffic management in weekends should be strengthened. There is a significant correlation between early in the month and FS injuries in pickup and truck crashes. Interestingly, this variable is positively correlated with FS injuries in rural pickup SV crashes (marginal effect 0.36%) and is negatively correlated with FS injuries in rural truck SV crashes (marginal effect \(-\)0.51%). In addition, late in the month has a significant positive influence on FS injuries in rural truck SV crashes (marginal effect 1.28%) but no significant impact was found on other vehicle types. Based on in-depth understanding of the literature, few research studies considered the impact of this variable on crash severity.