# A Path Recommendation Method Considering Individual Driving Preferences
## 3 A Path Satisfaction Model Considering Individual Preferences
### Path Satisfaction Model Based on Improved AHP Algorithm  
We utilized ALPR data as the foundation for constructing the AHP in this study. This section presents the methodology for constructing the path satisfaction model, incorporating improvements to the AHP. The subsequent explanation outlines the path satisfaction model in alignment with the research objectives of this paper. Based on the analysis conducted previously, pertinent indicators were organized and a four-layer model was constructed, taking path satisfaction as the main objective. The hierarchical model is visually represented in Figure 4.  
The path satisfaction model was structured into four layers. The target layer (\(A\)) represents path satisfaction. The criterion layer (\(B1\)) comprises individual driver attributes, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Matrix Order** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline RI & 0 & 0 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average consistency index. Figure 4: Path satisfaction hierarchy model. including stability of travel time (STT) preference, historical inertia (HI) preference, path difficulty (PD) preference, and traffic status (TS) preference. The importance score for B1 was determined based on the corresponding characteristics in the historical trajectories of drivers. The criterion layer (\(B2\)) primarily consists of dynamic and static path attributes, such as path travel time (PTT), path length (PL), number of path lanes (PLN), path turning index (PTI), number of path intersections (PI), average path density (PAK), maximum path density (PMK), and historical path similarity (HPS). The importance score for \(B2\) was determined by comparing the indicators for a driver's chosen path with all trajectories within the specified time period. The alternative layer encompasses the alternative path set, comprising historical trajectories between OD pairs and several shortest paths between ODs. It is important to note that the connections in Figure 4 and the inclusion relationships in Table 3 only represent the main lower-layer indicators that influence the upper-layer indicators. During the actual calculation process, each upper-layer indicator considered all lower-layer indicators as contributing factors. The indicators without any connections had the lowest importance. To improve the objectivity in evaluating the mutual importance of indicators, we proposed an enhanced process for the construction of comparison matrices. Instead of relying on expert scoring, we utilized ALPR data as the basis for establishing a comparison matrix for each individual driver. This approach aims to simplify the construction process and make it more objective. In the traditional AHP, the elements of the comparison matrix are assigned values on a scale of 1-9. This requires judging both the relative importance of indicators and the degree of importance, resulting in a cumbersome construction process. To address this, we simplified the elements in the comparison matrix by using three values: \(-1\), \(0\), and \(1\). These values characterize the degree of importance among indicators. We reference the work of [PERSON] et al. [28], which inspired this simplification. The indicators affecting the comparison matrix \(A\) included all indicators in the criterion layer \(B1\). Thus, the comparison matrix \(A\) was a \(4\times 4\) matrix:  
\[A= \begin{bmatrix}a_{11}&a_{12}&a_{13}&a_{14}\\ a_{21}&a_{22}&a_{23}&a_{24}\\ a_{31}&a_{32}&a_{33}&a_{34}\\ a_{41}&a_{42}&a_{43}&a_{44}\end{bmatrix} , \tag{20}\]