## 3 Methodology
### Structural Equation Modeling (SEM)  
SEM is a statistical method that is utilized for examining and evaluating multivariate causal relationships [74; 75]. SEM SEM is distinct from other modeling techniques in thatit explores the direct and indirect effects on causal relationships that have already been established [76; 77]. SEM is becoming increasingly popular for investigating transportation and road safety issues. Some researchers have used SEM in the field of road safety [78; 79].  
The objective of SEM in this study is to investigate the relationship between road geometry, including horizontal alignment and vertical alignment, and the intersections with road accidents on Jordan's Desert Highway. SEM can interpret the correlation through a structural model, demonstrating the interrelationship between the indicators and latent variables. In this study, the model is developed as a reflective-formative model because the indicators fully determine the \"latent\" construct. The model's structure consists of three independent latent variables, which are: horizontal alignment, vertical alignment, and intersection. These are linked to a single dependent variable, road accidents. The theory is represented by latent variables (factors) in SEM, while data from measures (indicators) are fed into statistical studies to demonstrate the connections between latent factors. As the study focuses on forecasting, Partial Least Squares (PLS) is the ideal method for developing SEM [80].  
Thirteen indicators from the evaluation were assessed for their factorability and extracted as latent variables. Next, the researchers applied Exploratory Factor Analysis (EFA). Horizontal alignment, vertical alignment, and intersections were the categories into which the latent variables were classified. Ten screened indicators were repeated as indicators for these three latent variables. All latent variables were evaluated for validity and reliability by assessing the measurement model for the model. Regarding road accidents, there exist three indicators, namely accident rate, fatalities, and severity index. The structural model evaluation was then applied to the model. Table 1 displays the constructed model's definitions. Four measurement models with four latent variables are incorporated into the proposed model. Each latent variable contains multiple indicators. Nevertheless, the measurement model for the latent variable road accidents contains a formative construct for the indicators. The other measurement model, meanwhile, is based on a reflective construct. The Goodness of Fit of the Model (GoF) criteria were also used for determining whether values of GoF were no fit (less than 0.1), small (0.1-0.25), medium, (0.25-0.36) or large (more than 0.36) enough to be considered as a globally valid PLS model [81]. Figure 5 depicts the concept model of SEM-PLS analysis for this study.  
In relation to the previously discussed construct definitions, the following hypotheses were proposed;  
**Hypothesis 1 (H1)**. _Horizontal alignment indicators have a significant effect on road accidents._  
**Hypothesis 2 (H2)**. _Vertical alignment indicators have a significant effect on road accidents._  
**Hypothesis 3 (H3)**. _Intersection indicators have a significant effect on road accidents._  
Figure 5: Conceptual model of SEM.