# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 6 Discussion and Conclusions
Meanwhile, several key areas can be further explored: (1) User-centric factors: Future research should thoroughly consider the individual differences among road users and how these variances affect the effectiveness of highway direction signs. This includes studying demographic factors, driving experience, and cognitive load, and exploring strategies to mitigate the negative impacts of these differences on sign usability. (2) External environmental factors: In evaluating the use of highway direction signs, it is essential to take into account external factors such as road alignment, traffic volume, other vehicles, and weather conditions. Research should focus on how to integrate these variables into the evaluation methods and correct their effects on assessment results. This may involve developing adaptive models that can dynamically adjust to changing conditions, ensuring a more accurate reflection of sign effectiveness under varying circumstances. Conceptualization, J.R. ([PERSON]) and Q.L. ; methodology, M.L. and Q.L. ; software, J.R. ([PERSON]); validation, J.R. ([PERSON]); formal analysis, M.L., J.R. ([PERSON]) and A.K. ; investigation, J.R. ([PERSON]); resources, D.Z. ; data curation, M.L., J.R. ([PERSON]) and D.Z. ; writing--original draft preparation, J.R. ([PERSON]), M.L. and Q.L. ; writing--review and editing, Q.L. and J.R. ([PERSON]); visualization, D.Z. ; supervision, D.Z. and A.K. ; project administration, A.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation General Project of Xinjiang (2024D01C30), the \"Tianchi Talent\" Introduction Plan Leading Innovative Talents Project of Xinjiang \"Study on Key Technologies for Optimizing the Quality of Expressway Traffic Safety Facilities and Enhancing the Lifetime Traffic Safety Guarantee in Special Areas and Complex Environments\", and Guangzhou University Science and Technology Graduate Research Project (2024312229). **Institutional Review Board Statement:** This research does not require ethical approval. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**  
TOPSIS Technique for Order Preference by Similarity to an Ideal Solution  
KMO Kaiser-Meyer-Olkin  
AHP Analytic hierarchy process  
CRITIC Criteria Importance Through Intercriteria Correlation  
EM Entropy method  
CI Consistency index  
RI Random consistency index  
CR Consistency ratio  
FCE Fuzzy comprehensive evaluation  
GRA Grey relational analysis  
DEA Data envelopment analysis  
FA Factor analysis  
(1) Tables in the questionnaire survey \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Primary Indicator** & **Secondary Indicator** & **Relative Weight W\({}^{\emptyset}\)** & **Total Weight W\({}_{\mathbf{j}}\)** \\ \hline \multirow{3}{*}{Normativity} & D\({}_{1}\) & 0.333 & 0.1498 \\ \cline{2-5}  & D\({}_{2}\) & 0.333 & 0.1498 \\ \cline{2-5}  & D\({}_{3}\) & 0.333 & 0.1498 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Pairwise comparison matrix of primary indicators. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**B\({}_{4}\)** & **D\({}_{16}\)** & **D\({}_{17}\)** & **D\({}_{18}\)** \\ \hline D\({}_{16}\) & 1 & 1/4 & 1/3 \\ \hline D\({}_{17}\) & 4 & 1 & 2 \\ \hline D\({}_{18}\) & 3 & 1/2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison matrix for secondary indicators corresponding to “Consistency”.