## 3 Methodology  
In the broadest terms, the creation of an AHP model follows a process consisting of the following steps (Figure 1), detailed further within the section. To begin, the model itself needs to be structured. Since it is a hierarchal structure, the model needs to be separated into layers, or \"levels\", in order of priority (Figure 2). The top level of any AHP model should be the overall goal of \"the most sustainable\" alternative or solution. This would then be followed by Level 2, where all the involved parties and perspectives are identified, followed by the sub-criteria of each perspective in Level 3. Finally, the potential alternatives will be identified and listed in Level 4, where all criteria will contribute to their priority in different weights to be decided. Subsequent levels can be created for each criterion, depending on the complexity of the problem, but for the purposes of this paper, four levels will suffice. Once completed, the data then need to be collected and compiled. With the data compiled, the next step is to determine the weights of each perspective and criteria by using pairwise comparisons. Pairwise comparisons are where each element of the respective levels is compared against each other element in pairs until each possible combination is ranked in terms of priority. In this paper, the ranking is performed with Saaty's Intensity Scale. The third step of the AHP is to determine the weight from the pairwise comparisons between all the elements. This can be achieved by arranging elements into a square matrix [PERSON], [PERSON] et al., [PERSON] et al., [2; 3; 4]. Each element of the matrix's diagonal will be given a value of 1. In order to compute the importance of the elements, a square matrix is developed as follows:  
\[A= \begin{bmatrix}\frac{a_{1}}{a_{1}}&\frac{a_{1}}{a_{2}}&\cdots&\frac{a_{1}}{a_{n }}\\ \frac{a_{2}}{a_{1}}&\frac{a_{2}}{a_{2}}&\cdots&\vdots\\ \vdots&\cdots&\ddots&\vdots\\ \frac{a_{n}}{a_{1}}&\cdots&\cdots&\frac{a_{n}}{a_{n}}\end{bmatrix} \]  
where a1, a2..., and an are comparison weights obtained from step 2. Figure 1: Flowchart of standard creation of AHP model [2]. Figure 2: Graphical representation of simplest AHP hierarchy [2]. Followed by finding the vector of priorities, the priorities of elements are determined with the Eigenvalue method [15], with the goal of reducing the number of pairwise comparisons by solving for the principal Eigenvalue W of the matrix A, as follows:  
\[\text{AW}=\lambda_{\text{MAX}}\text{ W} \tag{1}\]  
where \(\lambda_{\text{MAX}}\) is the largest Eigenvalue of A matrix, and W is a normalized vector obtained by use of a set of linear equations. The final step is to measure the inconsistency of each judgment. The consistency ratio must be calculated to ensure the consistency of the decision-making process The consistency ratio is defined as  
\[\text{CR}=\text{CI}/\text{RI} \tag{2}\]  
where CI is the consistency index and RI is the random index. The CI for a matrix A is calculated as  
\[\text{CI}=(\lambda_{\text{MAX}}-\text{n})/(\text{n}-1). \tag{3}\]  
RI is a consistency index that depends on the number of pair comparisons. Ref. [2] provides a table of random consistency indices. If CR < 0.1, the level of consistency will be acceptable. With the model now characterized, testing can now begin. The overall goal of this research is to find \"the most sustainable\" alternative to a proposed traffic control strategy. The strategy selected, \"Exclusive Truck Platoon Lanes (ETPLs)\", involved creating an exclusive lane for freight trucks, where they will be organized into a convoy at a constant speed using Coordinated Adaptive Cruise Control (CACC) technology. In theory, the ETPLs would be installed along all major highways, such as US interstates and state roads. These lanes would, as the name implies, only allow freight trucks to enter and leave the lane to limit interaction with passenger vehicles.