# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods
### Individual Quantitative Evaluation of Road Performance
#### 2.2.2 Synchronous Standard Quantitative Evaluation of Multi-Index Road Performance  
In this section, an accurate synchronous multi-index quantitative evaluation of road performance modeling system is established by maximizing the advantages of solving dimensions based on a T-S fuzzy neural network [39; 40], whose architecture is shown in Figure 5.  
The evaluation model using the \"IF-THEN\" form \(\mathbb{R}^{\tau}\): IF \(f_{o1}\) is \(n_{o1}^{i_{1}}\),\(f_{o2}\) is \(\mu_{o2}^{i_{2}}\),\(\cdots\)\(f_{ok}\) is \(\mu_{ok}^{i_{k}}\) THEN \(y_{\sigma\tau}=p_{o0}^{i_{1}}+p_{o1}^{i_{2}}f_{o1}+p_{o2}^{i_{3}}f_{o2}+\cdots +p_{ok}^{i_{k}}f_{ok}\), where \(o\in\{\mathbb{T},\mathbb{M},\mathbb{C}\},k\in\{\left|\mathbb{F}_{\Gamma}\right|,\left|\mathbb{F}_{\mathbb{M}}\right|,\left|\mathbb{F}_{\mathbb{C}}\right|\}\) are the set of significant factors of time, economy, and comfort; \(f_{oj}\in\{\mathbb{F}_{\Gamma},\mathbb{F}_{\mathbb{M}},\mathbb{F}_{\mathbb{C} }\},j=1,2,\cdots k\), \(\mu_{oj}^{i_{j}}\) is \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Factor Types**} \\ \hline \multirow{6}{*}{Road factors} & \multirow{3}{*}{road self-factors} & 1. & road alignment, road length, road intersection number, road level, \\  & & road width, number of continuous turns, road evenness, isolation of \\  & & green bels, and scribing isolation. \\  & & 2. & the ratio of green to credit, road traffic density, road evenness, \\  & & number of crossings, average speed of main roads, blocking rate of \\  & & intersections, average traffic delays and illegal occupancy Lane rates. \\ \cline{2-3}  & & 1. & road greening and landscape organization \\  & & 2. & gas station, parking lot, traffic indicator, trunk road lighting rate, \\  & & parking guidance and traffic guidance system setting \\  & & 3. & garbage field and operation station, pedestrian crossing facilities, \\  & & to sign and rate of population density circulation area \\ \hline \multirow{6}{*}{Subsidiary factors} & \multirow{3}{*}{law and regulation factors} & 1. & internship high speed limit \\  & & 2. & speed limit, limit height, weight limit, restriction entry \\ \cline{1-1}  & & 3. & single line setting, single and double number limit line \\ \cline{1-1}  & & 4. & influence, restriction, or prohibition of area and type of vehicle on the \\ \cline{1-1}  & & time of road separation \\ \cline{1-1} \cline{2-3}  & & 1. & restrictions on driving ability \\ \cline{1-1} \cline{2-3}  & & 2. & drive-age limit \\ \cline{1-1} \cline{2-3}  & & 1. & the fluctuation of the driver’s mood \\ \cline{1-1} \cline{2-3}  & & 2. & emergency (large-scale mass activities, major traffic accidents, \\  & & temporary traffic control) occupation rate \\ \cline{1-1}  & & 3. & weather, natural calamities, urban construction \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influencing factors of roads. Figure 4: (**a**) Initialize the valid range of the sample; (**b**) The corresponding plane projection of the initialization process; (**c**) Determining the effective radius of clustering; (**d**) The corresponding plane projection of the effective radius of clustering; (**e**) Effective sample screening results; (**f**) The corresponding plane projection of the result of Effective sample screening process. the fuzzy set of index \(o\); and \(p_{oj}^{i}\), \(y_{oj}\) denote the corresponding parameter and output of the fuzzy rule, respectively. The former network is used to calculate the applicability of fuzzy rules. For any \(f_{oj}\), the membership degree of the fuzzy layer is first based on the Gaussian membership function shown in Formula (12), where \(a_{j}^{i}\) and \(b_{j}^{i}\) represent the function centers and widths, respectively; \(i=1,2,\ldots,n\) where \(n\) is the fuzzy division number of \(f_{oj}\). To satisfy the actual factors that affect the situation, \(n\) is assumed to be seven levels that represent the seven effects of - - -, - -, 0, +, ++, and +++, respectively.