## 2 Literature Review
Based on pairwise comparisons (PCs) of drivers' responses to the driver behavior questionnaire (DBQ), the FAHP process calculated the weight factors and rated the significant driver behavior criteria. According to their study's findings, \"violations\" were the most important driver behavior standard for level 1 in all nominated regions, with the exception of Hungary. All regions, with the exception of Turkey, observed \"aggressive violations\" to be the most important driver behavior criterion for level 2. Additionally, level 3 drivers in Hungary and Turkey rated \"driving while intoxated\" as the most important driver behavior criterion. [PERSON] and [PERSON] (2022) [PERSON] and [PERSON] (2022) applied the Pythagorean fuzzy analytic hierarchy process (PF-AHP), an expanded method of ordinary fuzzy sets, to evaluate and rank important driver behavior factors built into a hierarchical model using information acquired from drivers' groups observed in Budapest, Hungary. They claimed that the PF-AHP approach was a helpful method to evaluate driver behavior uncertainty while handling complex road safety issues. [PERSON] and [PERSON] (2018) [PERSON] et al. (2018) used the analytic network process (ANP) to analyze the inter-organizational complex system's behavior to identify major shortcomings in the lead agency of a country for road safety. They applied their developed ANP model on the Road Safety Commission (RSC) of Iran to identify the synthesized influence of factors on each other and suggested a complementary process to specify institutional improvements to prevent any organizational inefficiencies. [PERSON] et al. (2018) [PERSON] et al. (2018) devised an analytic network process (ANP) along with statistical methods to evaluate the overall degree of highway safety for Chinese roadways. They analyzed quantitative and qualitative indices (variables), along with factors that influence safety, such as collisions, intersections, alignments, and other important factors. They reported that ANP can take these elements into account, be used to elicit judgments, and generate ratio scales for use in basic arithmetic operations. [PERSON] et al. (2013) [25] studied the prioritization of various elements that can have an impact on the rate and severity of crash issues. They used 15 different safety indicators with a case study in Iran. They applied three different \"multiple criteria decision making\" (MCDM) methods, viz., analytical hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), and simple additive weighting method (SAW), and reported that AHP performed better compared to others. [PERSON] et al. (2021) [26] devised a combination of the best-worst method (BWM) and the analytical hierarchy process (AHP) method to compare and quantify the factors for frequent lane changing behavior in relation to road safety. The driver's replies to a predetermined questionnaire survey were used to prioritize the most important parameters impacting lane changing in order to demonstrate the applicability of the suggested model. They claimed that compared to the traditional AHP, their combined model saw fewer pairwise comparisons (PCs) and more accurate and consistent findings. Table 1 summarizes references for AHP methods reviewed in this paper. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of references for AHP methods used in traffic safety analysis. In Table 2, several variants of the AHP method used in the literature are compared with the proposed PBAHP. Traditional AHP is one of the methods more frequently used in traffic crash analysis to methodically assess the seriousness and significance of factors involved in traffic crashes. It uses pairwise comparisons and determines the relative importance of various variables, including the state of the roads, driving habits, vehicle features, and environmental factors [17; 18; 19].