## 5 Modeling Results and Discussion
### Discussion
Hence, these novel and interesting discoveries can further enrich existing research and can be used to effectively assign law enforcement duties--for example, increased supervision of pickups in the first 10-day period of a month and of trucks in the last 10-day period of a month in rural areas. According to the regression results, the probability of FS injuries in rural SV collisions varies with the seasons. More specifically, motorcycle and truck crashes in autumn are less likely to result in serious injuries (marginal effects \(-\)0.72% and \(-\)1.08%, respectively). This finding can be proved by existing research; for example, it was found that the probability of severe truck crashes and severe motorcycle crashes in autumn reduced by 0.07% ([PERSON] and [PERSON], 2017) and 0.03% ([PERSON], 2017), respectively. The coefficient of autumn is not statistically significant in other motor vehicle types. Further, passenger car, motorcycle, and pickup are more likely to be involved in severe rural SV crashes during the winter, with corresponding probabilities increasing by 1.04%, 1.94%, and 2.65%, respectively. However, there is no significant correlation between FS injuries and winter in rural truck SV crashes. These findings are indispensable and demonstrate the effectiveness of modeling rural SV crashes based on motor vehicle type to reveal the variable effects of risk factors. Meanwhile, the research results indicate that the traffic safety performance of rural roadways in winter is unsatisfactory and underscore the importance of traffic management during winter. A significant positive correlation between dark (without street lighting) condition and FS injuries in rural SV crashes was determined, which is supported by all vehicle types. The probability of FS injuries in dark (without street lighting) conditions increased by 3.81%, 3.90%, 3.47%, and 4.30% for passenger car, motorcycle, pickup, and truck crashes, respectively. A largely consistent finding can be found in ([PERSON], 2017), which represents this variable by complete darkness. Furthermore, there is no significant correlation between street lighting and FS injuries, which is supported by all vehicle types considered in this research. This can be confirmed by previous research [78]. These findings are valuable to improve the safety performance in rural areas. For traffic managers, the installation of streetlamps on rural roadways is indispensable; for ordinary drivers, cautious driving should be maintained under dark (without street lighting) condition. Almost all researches concluded that there is a significant positive correlation between collision with fixed object and severe rural SV crashes [21; 76; 79]; the same conclusion is drawn in this study. By modeling injury severity of rural SV crashes across different vehicle types, the destabilizing effects of risk variables can be revealed. In passenger car, motorcycle, pickup, and truck crashes, the probability of FS injuries caused by collision with fixed object increased by 7.37%, 26.13%, 8.98%, and 5.43%, respectively. Among them, the highest probability is maintained by motorcycle due to it not providing adequate protection. Further, rural SV collisions between motorcycle and fixed object could easily result in rollovers, which will cause riders to collide violently with hard objects such as the road surface and significantly increase the likelihood of serious injury. It has been widely reported that the principal cause of fatality among motorcyclists is head injury [80]. Wearing a helmet can effectively protect motorcyclists from head injuries [75]; however, 60.1% of riders in motorcycle crashes do not use helmets. Hence, it is necessary to strengthen the management of riders. In addition, roadside fixtures such as stone pillars in traffic infrastructure, which can effectively guide traffic operations, are a potential traffic safety hazard due to the collision energy that cannot be absorbed by the fixed object.