Optimization of Underground Cavern Sign Group Layout Using Eye-Tracking Technology

Round 1

Reviewer 1 Report

This research investigates the importance of efficient sign layouts in enhancing transportation safety in underground construction caverns. Previous studies have focused on optimizing individual traffic sign layouts by evaluating drivers' gaze behavior in tunnels. However, the lack of a theoretical framework for the visual perception of sign groups hinders the measurement of drivers' comprehensive visual perception and the layout optimization of sign groups. To address this, the research introduces a calculation method for sign group information volume and a visual cognition model, establishing a comprehensive evaluation approach for sign group visual cognition. Eye movement data collected using eye-tracking technology were utilized to evaluate the comprehensive visual perception and optimize the layout of sign groups. The findings suggest that excessive information may result in overlooking signs positioned on the left and top, and drivers cannot improve cognitive efficiency and driving safety even with self-regulation when faced with an information volume exceeding 120 bits within a 100-meter span. Overall, this study demonstrates the effectiveness of the proposed method in promoting the long-term safety effects of temporary signage layouts in underground construction areas. However, some points could be improved to enhance the added value and discussion of the paper:

1.      Clarify the visual cognition model and its components. The model is briefly described but not fully explained. More details on how each cognitive link works and how they are combined would strengthen the model.

2.      Expand on the selection of evaluation indicators. More justification is needed for why those specific indicators were chosen and how they measure the corresponding visual cognition aspects.

3.      Provide more details on the data collection and processing methods. The eye-tracking experiment and data analysis are briefly outlined but lack details on things like sample size, exact procedures, and statistical tests used.

4.      Expand on the results and discussion. While some key findings are highlighted, more in-depth analyses and interpretations of the results are needed. Discussing limitations, implications, and directions for future work would strengthen the paper.

5.      Improve the organization and structure. The sections lack proper subheadings and a logical structure to guide the reader through the methodology and findings.

6.      Provide more context for the study. Explain the rationale and background in more detail to set up the significance of the research.

7.      Review related work in more depth. Discuss prior visual cognition and signage design studies to position your study within the existing literature.

8.      Clarify the objectives of the research. Explicitly state the aim of the study and the specific research questions or hypotheses.

9.      Give more details on the sample and experimental design. Explain who participated in the eye tracking experiment and provide details on the stimuli, procedure, apparatus, and experimental conditions.

10.  Describe the data analysis in more depth. Explain the statistical tests used and how they address the research questions. Include a flowchart or diagram of the data processing steps.

Minor editing of English language required

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

In this paper, the authors aim to explore the optimization of sign layouts in underground construction sites. In particular, they developed a calculation method of sign group information volume and a visual cognition model to determine the comprehensive evaluation method of sign group visual cognition. They evaluated the comprehensive visual cognition of sign groups with different information volumes using eye-movement data required for collecting indexes using eye-tracking and made suggestions for layout optimization. The proposed approach is interesting and have merit, the paper is well-written and the presentation and language are good. However  I have the following comments:

1.     In the analysis of the proposed approach, the authors should compare their results with other existing methods, if not quantitatively, they can do it qualitatively. This is important to show why the proposed approach is better than the existing ones.

2.     A computational complexity of the proposed approach should be added.

3.     The resolution of the figures’ sub-captions should be improved.

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Minor editing of English language required

Minor editing of English language required