## 3 Methodology and Database
The use of AHP simplifies the complexity of decision analysis into the formulation of a matrix. This matrix allows for the comparative analysis of pairs of spatial databases, each representing the influence of a singular parameter in the field. The objective is to collectively assess the integrated influence of all parameters, considering the significance of each within the analysis process. The hierarchy of parameter influence within the matrix is established based on predefined numerical values, as outlined in Table 1[65]. This structured approach provides a systematic way to evaluate the combined impact of different factors influencing road incident outcomes. Utilizing the Analytic Hierarchy Process (AHP) method can diminish the subjectivity involved in interpreting comparisons among databases used in the spatial analysis. This is achieved through the application of the Consistency Index (CI) and the Consistency Ratio (CR), as defined in Equations (1) and (2) [65]. These metrics provide a structured framework to ensure more objective assessments in the comparative analysis of the databases. \[\text{CI}=(\lambda_{\text{max}}-\text{n})/(\text{n}-1) \tag{1}\]  
\[\text{CR}=\text{CI}/\text{RI} \tag{2}\]  
where \(\lambda_{\text{max}}\)--maximum eigenvalue, n--position of the database in the row/column of the matrix, RI--random index [65] (Table 2). The validation of the AHP matrix is determined by the Consistency Ratio (CR) index value. If the CR index for the matrix under analysis exceeds 0.1, it indicates a need to reassess the significance of the factors (spatial databases) within the matrix. Conversely, if the CR value is less than 0.1, the matrix is considered validated and can be utilized in spatial analysis. A CR value of exactly 0 indicates an ideal comparison of the databases within the matrix. The implementation of the Analytic Hierarchy Process (AHP) in this study is divided into two principal components, each structured to minimize subjectivity and reduce potential errors in the spatial analysis process. The first component involves the spatial integration of databases, which represent distances to traffic incidents. This integration is based on AHP and reflects the categorization established during the exploratory analysis phase. The spatial databases are integrated according to their assigned importance in the initial classification, resulting in the creation of 5 distinct spatial databases: 'Severity' (very severe incidents, severe, moderate, and minor incidents), 'Incident occurrence pattern' (head-on collisions, sideswipe/merging, skidding/aquaplaning, side-impact/rear-end collisions, car falls), 'Weather conditions' (normal, rain/wind/blizzard, fog, sleet/snow), 'Road Restrictions' (unrestricted, traffic restrictions, parking prohibition, limited traffic/one-way systems, traffic control (priority)), and 'Traffic signal system' (intersections, public transport stations, roadway alignment signs (turn, sharp curve, or curve), temporary traffic signals (roadworks, detour), and railroad crossing warning systems). The integrated geographical analysis phase concludes with the implementation of an Analytic Hierarchy Process (AHP) model, which assesses the 5 spatial datasets to determine \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ \hline RI & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 & 1.51 & 1.53 & 1.56 & 1.57 & 1.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Random consistency index (RI) [65].