## 1 Introduction
Consequently, urban local roads experience higher traffic density and a greater number of cars. In contrast, rural local roads typically have less traffic. Figure 2 has been constructed to visually represent the stark contrast between two distinct local road settings. In Figure 2, situated within the confines of an urban area, the roads are conspicuously flanked by parked cars on both sides. This parking arrangement exerts a considerable influence on vehicular speed and introduces a heightened risk of collision. Conversely, as depicted in Figure 2, the periphery of the city exhibits a markedly different landscape, characterized by sparse vehicular presence and unobstructed roadways, fostering high-speed travel. However, in such locales, when collisions transpire, the potential for severe outcomes, including fatalities, is significantly amplified. Another noteworthy distinction pertains to the presence of activity centers, such as shops and restaurants, within these settings. Here, urban traffic often necessitates halting, potentially leading to rear-end collisions. Although these incidents may result in relatively minor injuries, the risk remains palpable. Meanwhile, rural roadways primarily abut residences, rendering vehicular transportation superfluous and enabling uninterrupted high-speed travel. Additional factors include law enforcement, which is more rigorous in urban areas, exemplified by checkpoints, helmet requirements, and other measures. The differences also extend to engineering approaches; urban roads may receive more funding for pavement construction and safety equipment, which are often more standardized on urban roads than rural roads. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (Year)** & **Research Aims** & **Country** & **Road Type** & **Spatial Instability** \\ \hline \multirow{3}{*}{This study} & To identify factors influencing injury severity in young adult motorcyclists on local roadways, comparing urban and rural areas. & Thailand (2018–2020) & Local Road & ✓ \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & To investigate motorcycle crash severity, comparing young and old riders. & Thailand (2015–2020) & Local Road & - \\ \hline \multirow{2}{*}{[PERSON] [8]} & To analyze the impact of motorcyclists’ age on injury severity, considering three age groups. & Florida (2013–2017) & Not & - \\ \hline \multirow{2}{*}{[PERSON] et al. [9]} & To assess the link between age and severe injuries in young motorcycle riders. & Pakistan (2007–2015) & Not & - \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [12]} & To predict fatal motorcycle crashes and identify key contributing factors. & Israeli (2002–2008) & Not & - \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [13]} & To analyze motorcycle crashes in Spain based on gender and age. & Spain (2006–2011) & Not & - \\ \hline \multirow{2}{*}{[PERSON] et al. [3]} & To warrant a study to understand overall crash characteristics and associated risk factors. & Malaysia (2006–2008) & Not & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The studies of crash injury severity of Young Adult Motorcyclists. Figure 1: Motorcycle crashes on local roads in Thailand. Studies aiming to identify policies for reducing crash severity primarily employ the Logit Model Analysis method, which differentiates between the alternative outcomes: fatal and non-fatal crashes [14]. Currently, the Logit model has evolved to incorporate a more complex principle of random parameters, an analysis premised on the assumption that various factors may impact differing crash severity levels by examining parameter variance [15]. This is logical, as even though motorcycles may operate in the same area, crash severity levels can vary. The analysis, based on the principle of unobserved heterogeneity in means, seeks to determine how each random parameter variable is influenced by specific factors (factors not analyzed for their effect on crash severity), either increasing or decreasing the mean of the random parameter.