PONE-D-23-32679Predicting Black Ice-Related Accidents with Probabilistic Modeling Using GIS-Based Monte Carlo SimulationPLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The theme approached in the paper is relevant. The main strong points of the work are: (1) the theme is current and strategic involving aspects related to accident prevention and strategies to prediction; (2) the general quality of the text and more specifically the quality of the results (section 3) and their discussion (section 4). The main weak points are: (1) the little discussion of the scientific contribution of the work (comparison with state of the art); (2) the discussion of related works was restricted to describe the researches without a consistent comparison and discussion of the scientific contribution of this paper. The discussion is restricted to last paragraphs of Section 1 but without a more detailed comparison with related works. Table 1 compares the proposal with only one work.

During the article revision, I found aspects that must be considered in a future version. I will list them in the same order they appeared during the reading. I hope the comments can be used to improve the text. At the end, I will present my final evaluation about the work.

1) The abstract must be improved to clarify the scientific contribution of the paper. The authors discuss several aspects of the work, but what is the difference in relation to related works (state-of-the-art)? The reader ends the abstract without know what is the scientific contribution of the proposal;

2) It is important to include in the section "Introduction", a more complete description of the paper’s proposal. The section discusses several topics, but one of the most important aspects of a scientific paper is not presented, namely, the scientific contribution based on a comparison with related works. There is a discussion of related works and a table with only one work produced also in South Korea, but I recommend improving the discussion, mainly amplifying the table. In addition, the authors can include a “research question” to indicate the focus of the research. Based on the question, the reader can understand the contribution that is being sought;

3) I believe the following aspect is the most relevant weakness of the paper. Section 1 does not sufficiently compare the related works with the proposal. There is an initial discussion, but I believe that it would be interesting to improve the organization through a “comparison with the proposed work”. Typically, based on a revision of related works is possible to indicate the contribution sought by an article. I advise the authors to improve the comparison table (table 1) with more works. Based on this table, it would be easier to understand the relation between the works, their relationship with the proposal and the scientific contribution of this research. Perhaps, this discussion can be included in a specific Section 2 dedicated to “Related Works”;

4) I recommend the authors to enrich the article through the inclusion of a discussion and references related to “Contexts” and more specifically about two emergent research topics called “Context Histories” and “Context Prediction”. Context histories are time series of Contexts that can be used to several kinds of data analysis such as Context Prediction and Similarity Analysis. The authors can explore these topics as future works applying these strategies on their work, mainly for recording in context histories the history information related to the roads (for example, air temperature, precipitation, snow, traffic) and using these context histories to several kind of analysis, including “Context Prediction” and “Pattern Analysis” (considering conditions that can generated Black Ice). Section 5 can be improved with future works regarding these strategic themes. I recommend using the following references to indicate this research way. These references involve the definition of context histories, context prediction, similarity analysis of context histories and pattern discovery on context histories: (1) A Multi-Temporal Context-aware System for Competences Management. International Journal of Artificial Intelligence in Education, 2015. https://doi.org/10.1007/s40593-015-0047-y; (2) ORACON: An adaptive model for context prediction. Expert Systems with Applications, 2016. https://doi.org/10.1016/j.eswa.2015.09.016; (3) A Risk Prediction Model for Software Project Management based on Similarity Analysis of Context Histories. Information and Software Technology, 2021. https://doi.org/10.1016/j.infsof.2020.106497; (4) CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories. Pattern Analysis and Applications, 2020. https://doi.org/10.1007/s10044-019-00829-9.

5) Section 2 describes the methodology. The first aspect that must be answered is why the Monte Carlo simulation was chosen to make the predictions in this proposal. There are several other strategies to conduct predictions. In addition, it is important to explain how GIS was used in the methodology and why it was used. Moreover, Figure 2 and Table 2 must be better explained. In Section 2.2.1, the authors must clarify how the actual data indicated were acquired and organized. Table 4 has a general vision, but I recommend to better explain this step of the research. Can that kind of information be treated as Context information (see the item 4 in this review)?

6) Section 3 presents the results. I recommend in this section to better describe why the results are relevant to the scientific contribution of this research. In addition, the text indicates three results, but it presents only two (subsections 3.1 and 3.2);

7) In section 4, firstly, I recommend including an introduction (between titles 4 and 4.1). I believe that section can be improved with an initial discussion about the relation of the results with the scientific contribution and with the research question if this question was included in the “Introduction” as recommend in this review. I also recommend summarizing the main results to simplify the reading and better organize them. For example, the authors can include at the end of the section a table dedicated to present the main lessons learned in the work. I also recommend including a discussion of difficulties encountered and limitations of the research;

8) Finally, I advise the authors to improve the "Conclusions" (Section 5) through a more complete discussion of future works. As indicated in the item 4 of this review, the authors can propose future work based on Context and analysis of Context histories to predict contexts related to Black Ice. They already have indicated in the text the interest in time-series modeling techniques. Context histories are time-series that organized information related to Contexts. These themes are aligned with the research presented in this article.

Based on these comments, I can present my final evaluation. I think the article can be accepted, after the improvements indicated in this review. I consider that that paper will need “Major Revision”.

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Reviewer #1: No

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Predicting Black Ice-Related Accidents with Probabilistic Modeling Using GIS-Based Monte Carlo Simulation

PONE-D-23-32679R1

Dear Dr. Yoon,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Abel C.H. Chen

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I revised the article and the review letter.

The text has been significantly improved, mainly through new references and a better discussion of the scientific contribution.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

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