Incident Analysis in Micromobility Spaces at Metro Stations: A Case Study in Valparaíso, Chile

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript examines passenger safety incidents in micromobility spaces at Valparaíso Metro stations, focusing on the period between 2022 and 2023. It aims to identify factors influencing incidents, such as station design, passenger flow, and weather conditions.

1.        The choice of Valparaíso Metro as the case study needs more explanation. Why is it exceptionally suited for this analysis compared to other metro systems?

2.        The data analysis focuses primarily on regression models without exploring other potential methods like machine learning. Are these models enough? Please justify.

3.        More explanations about the theoretical background of the model used are needed.

4.        Provide a clear rationale for using a dataset of 500 incidents. How representative is this sample of the overall incident landscape in the Valparaíso Metro system?

5.        The manuscript reports R² values but does not discuss their implications. Low R² values for some models suggest poor explanatory power. Discuss the limitations of these models and potential improvements.

6.        There is no mention of validation of the model results.

7.        The discussion section is fragmented and does not effectively synthesize the results with the existing literature. Strengthen this section by integrating your findings with prior studies.

 

8.        The conclusion overstates the generalizability of the findings.

Author Response

Dear Reviewer,

Thank you very much for all the comments. We tried to address all of them as best as possible. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This article (sustainability-3322005) analyzes passenger accidents at the Valparaíso metro station in Chile and their relationship with safety factors. The main objective is to examine how station design, passenger flow, and weather conditions influence the frequency and types of accidents across various micro-mobility spaces. Using data from the Valparaíso metro for 2022-2023, approximately 500 accident records were reviewed to assess the impact of factors such as peak hours, weekdays, and platform design. The study is interesting. Below are comments for further improvement. 

Concerns:

1. Ensure a unified color scheme for all images. For example, Images 5 and 6 are both bar charts, but the noticeable color differences affect the overall aesthetics of the article.
2. In Section 3.2, two regression models are constructed to analyze the binary variable "incidents." The authors should consider providing more explanation about the selected regression models and elaborate on the rationale behind the indicators used to assess model performance. In Section 4.2, clarify what model was used and how the results were derived from the correlation models.
3. Please provide a detailed introduction to this model, including the reasons for its selection and its advantages.
4. Building on the previous point, when stating that the model performs best at R²=0.75, consider providing a comparison with the model's performance at other R² values to illustrate how it behaves under different conditions.
5. In Section 4.3, where the article classifies risk probabilities related to accident frequency and proposes safety measures, please provide more specifics on the recommended safety measures to be implemented.

Author Response

Dear Reviewer,

Thank you very much for all the comments. We tried to address all of them as best as possible. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This is research on incident analysis in a micromobility environment in metro stations, based on a case study in Chile. I found the research very interesting and quite enlightening. The paper's structure is logical and easy to follow. The case study design is solid, with academic and professional visualizations. So, overall, I think it's a good piece of work. However, I have some minor observations that might be taken into consideration:

 

1. What type of logistic regression is performed (binary, multinomial, or ordinal)? What are the specific independent and dependent variables used in each respective model? How are the variables coded and standardized? How are the terms "Warm Seasons" and "Cold Seasons" defined in terms of specific months or temperature ranges?

 

2. Why does Quilpue station have the highest number of incidents? What specific factors contribute to this? 

 

3. Tables 4 and 5 seem arbitrary in terms of the probability and impact levels chosen. What is the basis or reasoning for the levels picked? How are the threshold levels obtained?

Author Response

Dear Reviewer,

Thank you very much for all the comments. We tried to address all of them as best as possible. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

• While the regression models are valuable, the interpretation of coefficients, especially for the Flow variable, lacks depth. A coefficient of zero, though small, suggests an underlying issue with data granularity or model fit, which should be further explored. Additionally, the limited variance explained (R² = 0.75 for the total data model) suggests unaccounted factors or complex interactions that might require advanced modeling techniques.
• The study mentions future incorporation of other factors. However, the absence of real-time crowd behavior analytics or psychological stress indicators (e.g., commuter impatience, crowd-induced anxiety) limits the practical application of the findings. Integrating predictive elements like crowd density and station layout simulations could enhance understanding.
• The post-pandemic period may have affected passenger behavior, which could skew the findings. Addressing this explicitly in the study’s limitations would add depth to the interpretation of trends observed.
• Please refer related research "Research on entropy based corrective maintenance difficulty estimation of metro signaling"

Author Response

Dear Reviewer,

Thank you very much for all the comments. We tried to address all of them as best as possible. Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for your revisions and for addressing all the comments and suggestions provided during the review process.

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks for the authors' revision (sustainability-3322005). The paper was improved a lot. 

I ensured that the authors addressed the concerns raised appropriately and improved the paper. I am satisfied with the current version and would like to recommend it to be accepted for publication.

It would be much better if the authors could promote the quality of the figures like the resolution ratio.

Comments on the Quality of English Language

The language level should be improved further. 

Author Response

Dear Reviewer,

We have improved the English and also the quality of the images.

We appreciate your feedback.

Best regards,