## 3 Methodology
### Highway Rockfall Susceptibility  
The highway rockfall susceptibility index is the internal factor that leads to the occurrence of rockfall. The rockfall may be influenced by a series of factors. The scientific selection of factors is a key step in rockfall susceptibility evaluation studies. Recently, a number of scholars have selected different elements to assess rockfall susceptibility, and we have counted statistics on them, as shown in Table 1.  
This study posits that the evaluation index for rockfall susceptibility should comprehensively reflect the topographic and geomorphological characteristics, vegetation cover, geological condition, and rock weathering condition. The approach is informed by established evaluation indexes from previous research, emphasizing the scientific rigor, applicability, and accessibility of the data employed. Finally, we selected the indicators shown in the table above. We believe that these elements are the most common and the main internal factors leading to the occurrence of rockfall accidents. Among these 8 evaluation indexes selected for this study, elevation, slope, aspect, and relief amplitude are utilized to characterize the topographic and geomorphological characteristics of the study area, effectively indicating the stability of rock and soil [54]. The NDVI serves to assess vegetation cover, with denser vegetation providing significant protection for slopes  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Article** & **Slope** & **Elevation** & \ \begin{tabular}{c} **Relief** \\ **Amplitude** \\ \end{tabular}  & **Aspect** & **NDVI** & **Lithology** & \begin{tabular}{c} **Distance from** \\ **Fault** \\ \end{tabular}  & **RWI** \\ \hline [PERSON] et al. (2021) [41] & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \\ [PERSON] et al. (2021) [42] & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & & \\ [PERSON] et  
and reducing the likelihood of geological disasters [55]. Lithology and distance from fault are employed to represent the geological conditions of the area, with poorer geological conditions correlating with an increased probability of geological disasters [41]. Additionally, the RWI reflects the weathering of rock masses, and a higher probability of landslides, such as rockfall events, is observed with more severe weathering conditions [56; 57]. Recently, many studies have used different methods for evaluation, but the most important of them are machine learning methods, such as neural networks, support of random vectors, etc. Although these methods are the mainstream methods, they require a large amount of data for training, and the models built are not very interpretable. We combine the commonly used informative method with logistic regression. The information content method can determine the influence of different intervals of elements on the probability of rockfall accidents, while logistic regression can analyze the influence of each element on rockfall accidents. The combination of these two methods can accurately predict whether a rockfall accident will occur without using a large amount of data. According to the index of rockfall susceptibility shown in Table 1 and the actual data available to us, we finally chose the following factors for this study: elevation, slope, NDVI, aspect, distance from fault, relief amplitude, lithology, and RWI. The highway rockfall susceptibility index is shown in Figure 5.  
1. Elevation. Elevation is the absolute distance from a certain point along the vertical direction to a certain point. It is an important factor influencing the occurrence and development of geologic hazards such as rockfall and their morphological characteristics [41]. The greater the elevation, the greater the gravitational potential energy of the rockfall.