## 2 Literature Review
The traditional AHP methods, however, do not consider the actual occurrence or practical impact of each criterion when making a choice, which can produce biased results [27]. FAHP expands AHP by introducing fuzzy logic and helps decision makers deal with the language evaluations and inherent uncertainties that come with traffic crash investigation [20; 21]. The FAHP enables a more flexible and thorough assessment of crash variables by utilizing fuzzy numbers and logic to better capture the subjective assessments. Another extension is P-FAHP, which uses Pythagorean Fuzzy subsets, considering the degree of membership and nonmembership of factors [22]. The ANP method adopts a more comprehensive approach by taking into account both the interdependencies and the hierarchy of crash reasons. ANP captures the interconnectedness of crash causes, offering a comprehensive view of crash analysis and facilitating a more in-depth comprehension of system dynamics. Despite the advantages of the AHP's multi-criteria decision-making technique, some limitations are frequently present [28]. In this present study, proportion-based or percentage-based AHP (PBAHP) is proposed, which is a different approach that considers the relative importance of each criterion in the decision-making process and has been offered as a solution to this problem. PBAHP can be promising as a method for identifying the main causes and types of major crashes. More details about this technique are provided in the proceeding sections of this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **AHP Methods** & **City/Country** & **Comments** \\ \hline [PERSON] et al. (2013) [25] & AHP, TOPSIS, and SAW & Iran & Applied AHP, TOPSIS, and SAW methods and reported that AHP performed better compared to others \\ \hline [PERSON] et al. (2021) [26] & Best-worst method (BWM) and the analytical hierarchy (AHP) & Budapest, Hungary & Compared to the traditional AHP, their combined model saw fewer pairwise comparisons (PCs), resulting in more accurate and consistent findings \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **References** & **Description** & **Key Features** & **Comments** \\ \hline Analytical & [PERSON] et al. (2011) [17], & Use pairwise & & May suffer from inconsistency in pairwise comparisons \\ hierarchy process & [PERSON] et al. (2020) [18], & & & \\ [PERSON] et al. (2022) [19] & & & & \\ \hline Fuzzy analytical & [PERSON] et al. (2009) [20], & Extension of AHP that incorporates fuzzy logic to handle uncertainties and vagueness & Fuzzy scale on component weight, fuzzy set theory & Requires expertise in fuzzy logic and fuzzy set theory \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of variants of AHP with the proposed PBAHP method. Overview of the Analytical Hierarchy Process (AHP) and the Proportion-Based Analytical Hierarchy Process (PBAHP)  
AHP is a multicriteria decision-making tool which has been used in a wide range of applications. These applications include social and political sciences, as well as engineering processes [29]. The literature shows that AHP was first proposed by [PERSON] as early as 1972. Since then, the process has adopted many developments, enabling its greater use in different fields. These include the establishment of ranking scales, inconsistency limits and indices, and the development of easy-to-use exclusive software packages for AHP [30]. AHP is a measurement tool which works on pairwise comparisons based on fixed scales. The measuring scale can use discrete ratings of actual measurement of user preferences [31]. The problem is divided into two levels; the first level is the \"criteria\", and the second level is called the \"goal\". Criteria refers to the factors that affect the choice or the outcome, while goal is the choice or outcome of the process.