## 2 Methodology
### Data Analysis  
At the conclusion of the experiment, both survey and eye movement data were collected for subsequent analysis and evaluation using the Entropy Weight Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).  
In 1948, [PERSON] introduced the concept of information entropy, which characterizes the information contained in random events for the first time in information theory [34], and it is now widely used in the fields of economics, engineering, etc. [8,35]. EWM determines the weights of evaluation indicators by analyzing information entropy and the degree of variation among these indicators. Greater variation among indicators results in higher weights being assigned to those with more substantial information content.  
In this experiment, the EWM followed a general process. The original data matrix \(A=\left(a_{ij}\right)_{m\times n}\) was constructed based on the evaluation data and then standardized to \(B=\left(b_{ij}\right)_{m\times n}\). Following the calculation of the entropy weight \(w_{j}\), the weighting matrix was computed as \(c_{ij}=b_{ij}\ast w_{j}\).  
Consequently, this method effectively elucidates the contribution of various characteristics of directional signage to the evaluation outcomes. By mitigating the influence of subjective weighting, it enhances the assessment of directional signage effectiveness across diverse transportation hubs.  
The survey was meticulously designed around the variables of objective visual recognition, signage setting, information expression, and visual attention. [PERSON]'s five-level scale method was employed for question setting, and a linear weighting method was utilized to rank each evaluation, facilitating a comprehensive assessment of the effectiveness of guidance signage in different hubs.  
TOPSIS was subsequently applied to compare the importance and order of importance of different evaluation variables in distinct cities. It was proposed by [PERSON] et al. in 1981 and could be used to solve multi-object decision-making and evaluation problems [36]. TOPSIS proves highly effective in synthesizing multiple evaluation criteria for directional signage, including information content and visibility, thereby offering a precise assessment of signage performance.  
The fundamental idea involves calculating the distance \((d_{i^{\prime}}^{*},d_{i}^{0})\) of each evaluated object to the positive and negative ideal solutions. The relative closeness \((f_{i})\), derived from these calculations, signifies the proximity of the evaluated object to the ideal target.  
\[f_{i}=\frac{d_{i}^{0}}{d_{i}^{0}+d_{i}^{*}} \tag{1}\]  
Upon sorting \(f_{i}\) from small to large, the relative importance ranking of each evaluation index was obtained. Further calculations based on the standardized matrix of EWM, including the determination of positive and negative ideal solutions and relative closeness  
Figure 9: Experiment Process Based on Panoramic Videos.  
for each evaluation index, were performed to complete the importance ranking of each evaluation index.  
Given the simplicity and efficiency of the Entropy Weight Method and TOPSIS, the methods have garnered substantial attention from researchers addressing multi-criteria decision-making problems. Compared to methods such as AHP and fuzzy comprehensive evaluation, the approaches employed in this study offer enhanced objectivity in the evaluation process. Furthermore, they facilitate the integration of multi-dimensional data for weighting and ranking, thereby providing a more scientifically robust and comprehensive assessment.