# A Path Recommendation Method Considering Individual Driving Preferences
## 3 A Path Satisfaction Model Considering Individual Preferences
### Hierarchical Analysis Method  
The AHP [26] is a multi-criteria decision-making method proposed by [PERSON], an esteemed American operations researcher. The key feature of AHP is in its ability to quantify complex and fuzzy evaluation problems. This is achieved by breaking down objectives into layers and factors, comparing and calculating these factors within the same layer. In this process, weight values are assigned to each factor, providing a quantitative basis for evaluating the overall problem and ultimately selecting the optimal solution. In practical applications, the AHP consists of four essential steps. First, a hierarchical model is constructed to capture the relationships between the evaluation object and its related indicators. Second, a comparison matrix is constructed to establish the relative importance of these indicators. Subsequently, the weight-solution and consistency test are carried out to ensure accuracy and reliability. Finally, based on the quantitative results, the optimal solution is determined. 1. Building a hierarchical model  
The process of constructing a hierarchical model involves continuously deconstructing the original problem through logical analysis. This process entails breaking down the complex problem into important influencing factors, forming the next layer, and deciding whether further deconstruction is necessary based on the actual situation. By following this approach, the indicators are gradually refined according to different layers, resulting in the construction of a systematic hierarchical model, as depicted in Figure 3.  
In this paper, when constructing the model for individual travel satisfaction, we ensured the systematicity and completeness of the indicator system while refining it. This process led to the identification of several bottom-layer indicators. A complex problem is typically decomposed into three layers:  
The first layer is the target layer, which represents the primary objective that needs to be quantitatively described. The construction of the layer analysis model aims to determine the magnitude of each layer indicator's impact on the target layer, quantitatively representing this impact for ease of selection or decision making. The second layer is the criterion layer. The criterion layer represents the objectives or criteria that must be followed to solve a problem or evaluate an objective. It establishes a link to the upper layer, providing a basis for decision making, and serves as a refined indicator of the upper layer's objective. Additionally, the criterion layer extends downward, generating more refined and detailed indicators. The third layer is the alternative layer, which is often the bottom layer. The alternative layer comprises potential solutions related to the target layer and forms the subject of decision making in the hierarchical analysis. Indicators within the alternative layer are  
Figure 3: Hierarchical model. usually more intuitive and clear, allowing for easier quantitative analysis. This enables the qualitative judgment of the target layer to be translated into quantitative calculations. 2. Construction of comparison matrix  
Hierarchical analysis facilitates the decomposition of complex problems during the model construction process. The hierarchical model comprises secondary indicators, tertiary indicators, and even more detailed sub-indicators at the fourth and fifth layers. However, it is important to note that the indicators in the same layer may not be of equal importance. When faced with multiple indicators in the upper layer, decision makers are typically tasked with determining their relative importance. This often relies on their experience, logical reasoning, and scientific judgment to assign value to different indicators, thereby transforming qualitative issues into quantitative ones.