# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 2 Method
### Comprehensive Evaluation Using the TOPSIS Method
#### 2.4.2 TOPSIS Method Overview and Steps  
TOPSIS is a widely used multi-criteria decision analysis method that ranks alternatives based on their proximity to the ideal solution [38]. The two core concepts in TOPSIS are the \"ideal solution\" and the \"negative ideal solution\": The ideal solution is a hypothetical option where all attribute values are optimal. The negative ideal solution is a hypothetical option where all attribute values are the least favorable. The ranking of alternatives is determined by comparing their distances from the ideal and negative ideal solutions. An alternative that is closest to the ideal solution and furthest from the negative ideal solution is considered the best option. TOPSIS is commonly applied in fields such as transportation planning and product selection, where a clear ranking of alternatives is needed. In this study, we adopted TOPSIS due to its ability to clearly rank alternatives by positioning them relative to both the ideal and negative ideal solutions. This approach is particularly beneficial when evaluating expressway routes consisting of multiple links, as it allows for an intuitive and transparent comparison. While other evaluation methods provide comprehensive rankings, TOPSIS emphasizes the distinction between the best and worst alternatives, which is critical for decision-making in complex transportation planning.  
The fundamental principle of the TOPSIS method lies in evaluating alternatives by their proximity to the optimal and least favorable solutions. The following are the seven steps involved in implementing TOPSIS for system evaluation or decision-making: (a) Construct a decision matrix: Gather performance data for each alternative based on multiple indicators, ensuring all key metrics are captured. (b) Normalize the indicators: Normalize the decision matrix to eliminate the influence of different measurement units. (c) Determine indicator weights: Assign weights to the indicators based on the evaluation goals and expert knowledge. (d) Construct a weighted decision matrix: Multiply the normalized matrix by the weights to form the weighted decision matrix. (e) Identify the optimal and least favorable solutions: Determine the ideal and negative ideal solutions based on the best and worst values for each indicator. (f) Calculate the distance to the optimal and least  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Method** & **Description** \\ \hline FA & Condenses multiple quantitative indicators into composite indicators. \\ \hline AHP & Constructs a judgment matrix through pairwise indicator comparison to calculate weights. \\ \hline FCE & Uses fuzzy mathematics to evaluate indicators that are difficult to quantify. \\ \hline EM & Analyzes the amount of information and variability each indicator provides to compute weights. \\ \hline GRA & Measures the degree of correlation between standard values and assessed items. \\ \hline TOPSIS & Ranks alternatives by calculating the distance from the ideal and negative ideal solutions. \\ \hline DEA & Determines optimal weights based on real data to calculate the relative efficiency of decision units. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of common evaluation methods.  
favorable solutions: Measure the distance of each alternative from the ideal and negative ideal solutions. (g) Calculate the closeness coefficient: Evaluate the degree to which each alternative approaches the ideal solution and rank it accordingly.