Public Transport COVID-19-Safe: New Barriers and Policies to Implement Effective Countermeasures under User’s Safety Perspective

Round 1

Reviewer 1 Report

Interesting topic, very good literature review. 

Chapter 2.1. should be deleted, it is not necessary to explain what is COVID-19.

Chapter 3. DATA. What do you mean by "Description of displacement"? Do you mean displacement like travelling, mobility? Maybe a better term is mobility patterns and changes in mobility patterns before and during pandemic.

Chapter 5. Results.

Please explain what is Loads and Robust t-ration. This is very important for the reader for understand, since you don't explain these in the model section.

What is vehicle agglomeration? Crowd in vehicles? Where are B7 and C1.7 in table in the choice model - utility function? There are typos in Table 4. I guess measures C1.1-C1.7, are C2.1-C2.7 in the choice model? How do you explain no other explanatory variable resulted in significant estimates in model 1, except income?

Why did you focus only on measures related to vehicles? Why did not you add stop/station measures, which are very important. Some barriers are stop/station related (B2) and some others could be either vehicle either stop related (B3, B5 and B6), so measures must include stop/stations.

I am not sure about the measure C2.2 - Change activity start time. This is not PT system measure, it rather an external measure and thus maybe could be excluded in favor of other PT measures (e.g. separation of passenger flows in stations, contactless payment and entry / exit in vehicle/stations, etc.). C2.3 - Inform the number of passengers in vehicles should be renamed to C2.3 - Information on the number of passengers in vehicles.

Overall, I am not sure about practical applicability of the model. You managed to rank measures in choice model, but are these differences in loads and t-values statistically significant so we can be sure that ranking is good? Did you test model sensitivity to changes in variables etc.?

Author Response

REVISION REPORT

 

Manuscript ID: sustainability-1560487

Title: PUBLIC TRANSPORT COVID-19-SAFE: NEW BARRIERS AND POLICIES TO IMPLEMENT EFFECTIVE COUNTERMEASURES UNDER USER'S SAFETY PERSPECTIVE

 

Dear      Editor Irene Zhang,

 

We carefully addressed all suggestions given in the previous revision. In this way, we appreciate the positive feedback of reviewers. We next list the points raised by the reviewers and explain how we addressed them. We will be happy to further review any point the reviewers do not see as properly addressed in the current version. Finally, we would like to thank the reviewers for the valuable inputs and for the time devoted to our work.

 

Best regards,

The authors

 

 

Answers to REVIEWER #1:

    

Overall Comments:  Interesting topic, very good literature review

Answer: Thanks for the positive evaluation. We addressed the other points raised below.

 

R#1 – Comment#1: Chapter 2.1. should be deleted, it is not necessary to explain what is COVID-19.

R#1 – Answer#1:  We understand the reviewer's concern. To address this issue, we removed Chapter 2.1. However, we have kept some information about Covid-19 that it helps to contextualize the work.

 

R#1 – Comment#2: Chapter 3. DATA. What do you mean by "Description of displacement"? Do you mean displacement like travelling, mobility? Maybe a better term is mobility patterns and changes in mobility patterns before and during pandemic.

R#1 – Answer#2: Thank you for the suggestion. Indeed, mobility patterns and travelling are better term for what the study aims to evaluate. We correct the terms throughout the text.

 

Chapter 5. Results.

R#1 – Comment#3: Please explain what is Loads and Robust t-ration. This is very important for the reader for understand, since you don't explain these in the model section.

R#1 – Answer#3: Thank you for your suggestion. We include a new paragraph on the method session to explain how the models is evaluated including an explanation over the load and the robust t-ratio test. See below.

“The results are evaluated based on significance of the loads (. We test significance using the robust t.-ratio values. T-ratios are calculated dividing the load per the standard errors. Values over 1.96 indicated the load is      statistically significant at 95% confidence level.” 

 

R#1 – Comment#4: What is vehicle agglomeration? Crowd in vehicles? Where are B7 and C1.7 in table in the choice model - utility function? There are typos in Table 4. I guess measures C1.1-C1.7, are C2.1-C2.7 in the choice model? How do you explain no other explanatory variable resulted in significant estimates in model 1, except income?

R#1 – Answer#4: Thank you for the questions. We replaced agglomeration for ‘crowd’ or ‘high presence of user’ along the text to ease understanding. We also reviewed Table 4 to correct the typos. B1-B7, C1.1-C1.7, and C2.1-C2.7 represent the attribute alternatives for barriers and countermeasures that interviews ranked in the survey. We modeled three different hybrid models to statistically identify the order of importance of each barrier or solution presented to interviewees. In each model, we simultaneously estimated seven utility functions. Therefore, the B1-B7, C1.1-C1.7, and C2.1-C2.7 also represent the relative utility function estimated for each barrier or countermeasure. We added new headings in the choice models’ results tables to make this information clearer to readers.

Regarding the explanatory variables, we can say that income was the only personal characteristics that showed a clear influence on the perception. Wealthier individuals tend to have a more negative perception over public transportation quality. The same is not valid for gender and age. This means we have individual form different gender and different age ranges evaluating the quality of the PT different and then, the models could not capture any tendency of this characteristics clearly. We incorporated the explanation into the manuscript content.

 

R#1 – Comment#5: Why did you focus only on measures related to vehicles? Why did not you add stop/station measures, which are very important. Some barriers are stop/station related (B2) and some others could be either vehicle either stop related (B3, B5 and B6), so measures must include stop/stations.

R#1 – Answer#5: We agree that stop/station measures are essential to make users feel safe and a vital qualifier attribute for public transportation. In the first group, we opt to include countermeasures commonly adopted as a COVID-19 response and measure if users perceived these solutions as safe. Even though we can say that some countermeasures could also be either vehicle or stop related like C1.5 - Use of mask, C1.4 - Turn off the air conditioner, C1.1 - Blocking and demarcation of places, C1.6 - Availability of alcohol gel, and C1.7 - Temperature measurement. The second group of countermeasures, although more vehicle-related, most of them present implications for the stop/stations, like C2.1 - Discount for off-peak travel or C2.7 - Increase offer. 

 

R#1 – Comment#6: I am not sure about the measure C2.2 - Change activity start time. This is not PT system measure, it rather an external measure and thus maybe could be excluded in favor of other PT measures (e.g. separation of passenger flows in stations, contactless payment and entry / exit in vehicle/stations, etc.). C2.3 - Inform the number of passengers in vehicles should be renamed to C2.3 - Information on the number of passengers in vehicles.

R#1 – Answer#6: Thank you for your comments. The measures the reviewer suggested could be part of a new study. Nevertheless, the authors decided to include the change activity start time in the second group of countermeasures to consider also measures that have indirect impacts on public transportation, testing users' acceptance of other types of mobility solutions. Staggering activity start times is one of the commonly used travel demand management strategies with a high impact on public transportation demand. The results reinforce the authors' choice. C2.2 is the third countermeasure in the rank results, showing that users perceive value in an indirect public transportation solution that can reduce crowdedness in public transportation. We include a sentence in the text with this note (see below)

“C2.2 being the third countermeasure in the rank results, shows that users can also perceive value in an indirect public transportation solution that can reduce crowdedness in public transportation. Also, the public transport operators participate in regulatory agencies, so that they can be regulatory agents of a flexible schedule policy.”.

 

R#1 – Comment#7: Overall, I am not sure about practical applicability of the model. You managed to rank measures in choice model, but are these differences in loads and t-values statistically significant so we can be sure that ranking is good? Did you test model sensitivity to changes in variables etc.?

R#1 – Answer#7: Thank you for your question. The Hybrid choice model was used to estimate the weights of each barrier/countermeasure according to the ranking responses obtained from the survey. The model estimates the probability of choosing a given barrier/countermeasure based on the utility of that barrier/countermeasure relative to the utility of all. Then, this probability represents the weight of the barrier/countermeasure. The model is performed through a joint estimation considering different scale factors for each one (i.e., consistent with different variances for the error terms of their utilities) (Orútzar e Willumsen, 2011).      Thus, if the error variance associated with barrier/countermeasure n is the same as that corresponding to the reference one, the scale factor of group n will be equal to one. The seventh attribute in the rank was the one fixed in each model. The rank is originated by the load values, whereas the higher the value more important the alternative is for users. This is a common approach in studies that use a hierarchical choice model.

The results have high practicability for policymakers, transport planners, and public transport operators since they provide insights about the COVID-19 response measures perceived as safer. Eliminating barriers and applying measures following users' perceptions might avoid the migration of public transportation users to other transport modes due to the fear of COVID-19 contamination. Moreover, the loads for each barrier/countermeasure are the model's constants and should be keep      to      correctly predict market shares.      Combining observed data for each barrier/countermeasure      with the weight obtained for each one     , it      could be possible to create indicators and compare them with other regions where the survey has been applied.

 

References added to address that point:

Ortúzar, J.D.D., Willumsen, L.G., 2011. Modelling Transport, Modelling Transport. https://doi.org/10.1002/9781119993308

 

 

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

This paper assesses the impact of COVID-19 pandemic on urban public transport. I have many concerns as follows:

1. What is the motivation of the study? What is the problem definition? What are the gaps and limitations in the literature that this study addresses?
2. No “Related Work” section is presented. Many studies in the literature have investigated COVID-19 pandemic with public transportation. What is different about this study? A comparative study should be conducted for these related works and presented in a comparative table to evaluate these studies based on specific evaluation criteria/parameters.
3. What are the limitations/drawbacks of the related literature studies? What is missing in the literature that would this study fill in?
4. Consequently, what are the main contributions of this study? What is unique about this paper with respect to the current studies? The research gap in the literature is unclear.
5. Research questions, that drive the paper, should be built in the introduction from an ongoing and pertinent bibliography (up to 2022). Identifying a research gap is a must; key is showing its significance to the field.
6. Authors did not discuss the other possible approaches/techniques that could have been used and their limitations/drawbacks that made them choose the selected models throughout the manuscript specifically. No justification or evidence provided to justify the selected methods.
7. What does Figure 1 represent? What does a “framework” mean here? Does this show the relation between the different used characteristics and variables? The figure is confusing. What do the oval, circle, rectangle, round-sided rectangle shapes mean? The used notations are ambiguous and not standardized. I suggest restructuring the figure and standardize the representation of the notations with the adequate description and explanation of the nature of this figure.
8. Authors did not provide any information about the experimental environment that has been used to evaluate their proposed model.
9. How was the experimental evaluation performed? Authors did not clarify the experimental approach and the evaluation methodology that have been followed to provide the different comparison tables presented.
10. Authors did not specify on what basis they selected the experimental setting values.
11. Answer your research question in the conclusions; what did we learn compared with current, significant research (up to 2022)?
12. How can the outcomes of this study be measured? How can we quantify the results of this study in terms of numbers and percentages? How can we judge - without numbers - that this study adds a value to the literature? Authors should avoid using fuzzy wordings and use instead numbers and percentages to quantify their findings. Quantifying the outcomes should be represented clearly in the Abstract, Results Discussion and the Conclusion sections.
13. Authors should add a section to discuss the threats to validity of their work, in terms of the internal, external, statistical, and construct validity.
14. Internal validity of the proposed model should address the basis on which the parameters were determined to evaluate the proposed work.
15. What are the limitations of your work?
16. No future work is provided.
17. Overall, a serious enhancement is required through a clarified detailed motivation, problem definition, real research gap, experimentation, justified claims and approaches, deep analysis, quantified discussions and comparisons with current, significant research (up to 2022).

Author Response

REVISION REPORT

 

Manuscript ID: sustainability-1560487

Title: PUBLIC TRANSPORT COVID-19-SAFE: NEW BARRIERS AND POLICIES TO IMPLEMENT EFFECTIVE COUNTERMEASURES UNDER USER'S SAFETY PERSPECTIVE

 

Dear      Editor Irene Zhang,

 

We carefully addressed all suggestions given in the previous revision. In this way, we appreciate the positive feedback of reviewers. We next list the points raised by the reviewers and explain how we addressed them. We will be happy to further review any point the reviewers do not see as properly addressed in the current version. Finally, we would like to thank the reviewers for the valuable inputs and for the time devoted to our work.

 

Best regards,

The authors

 

Answers to REVIEWER #2:

Overall Comments: This paper assesses the impact of COVID-19 pandemic on urban public transport. I have many concerns as follows:

Answer:  We appreciate the time devoted by the reviewer to help us improve the manuscript. We address each of the points below.

 

R#2 – Comment#1: What is the motivation of the study? What is the problem definition? What are the gaps and limitations in the literature that this study addresses

R#2 – Comment#2: No “Related Work” section is presented. Many studies in the literature have investigated COVID-19 pandemic with public transportation. What is different about this study? A comparative study should be conducted for these related works and presented in a comparative table to evaluate these studies based on specific evaluation criteria/parameters.

R#2 – Answer#1-2: We agree that these points must be raised, so we have included these considerations in the first section, Introduction. In the last two paragraphs of the Introduction, we highlight recent research in the area (up to 2022)., research gap in the literature, and research questions (see below).

 

“As a result of the COVID-19 pandemic, major transport authorities around the world have reported up to a 95% reduction in users (Alex Kreetzer, 2020). Thus, researchers and specialists strive to analyze the consequences and impacts of SARS-CoV-2 and their different variants (Dzinamarira et al., 2022) in PT. Subbarao and Kadali (2021) suggested methodology for post-lockdown PT system operation: (a) creating a public database for screening, (b) strategy for public transport operating system, (c) control measures for the public at transit stations and vehicles, (d) public transport disinfection system, and (e) strengthening of the public transport system and addressing ridership issues. Mesgarpour et al. (2022) conducted a machine learning approach that violated an optimal range of temperature, humidity, and ventilation rate to maintain human comfort while minimizing the transmission of droplets. Aghdam et al. (2021) investigated the COVID-19 related behaviors in the public transport system; their results suggest that gender, type of vehicle, age, and SES were significant predictors of nonadherence to COVID-19 preventive behaviors in public transport during the pandemic. Still, some authors have attempted to understand public transport satisfaction in the post-COVID-19 pandemic (Dong et al., 2021). Although these researches have brought relevant contributions, it is essential to identify new barriers and potential solutions that prevent users' migration to less sustainable transport modes. To the best of our knowledge, most studies were carried out in other contexts and perceptions, cultural issues are different, as is the dependence and quality of the public transport system. In this way, researchers and experts in public transport could use scarce resources more accurately.

     In this context, some questions arise. Which are the new barriers to the use of PT created by the COVID-19 disease? Which immediate measures (countermeasures) can increase users' perception of protection while riding PT? How the preconceived perceptions of PT quality affect the perception of safety for the barriers and countermeasures understudy? Answering these questions can help governments and operators implement more effective actions to increase user perception that PT is COVID-safe. This research assesses the impact of the COVID-19 pandemic on PT under the users' perspective in the context of Latin American cities. The general perception that PT is unreliable and unsafe, the long commuting hours, and the more restrictive transportation option due to the strong impact of transportation cost on families budget (Roberts, 2014), brings to the discussion local specificities that affects user experiences before and after COVID-19. We aim to control the effects of the previous experiences with latent variables constructed from PT quality indicators. Using this data from an online survey applied to PT users throughout a metropolitan area in southern Brazil, we estimated three different hybrid discrete choice models to understand the barriers and countermeasures to contain the SARS-Cov-2 outbreak.”

 

References added to address that point:

Aghdam, F.B., Sadeghi-Bazargani, H., Shahsavarinia, K., Jafari, F., Jahangiry, L., Gilani, N., 2021. Investigating the COVID-19 related behaviors in the public transport system. Arch Public Health 183–183.

Alex Kreetzer, 2020. The Future Of Public Transport In A Post Covid-19 World - Iomob’s Scott Shepard [WWW Document]. Auto Futures. URL https://www.autofutures.tv/2020/05/07/the-future-of-public-transport/ (accessed 12.27.21).

Dong, H., Ma, S., Jia, N., Tian, J., 2021. Understanding public transport satisfaction in post-COVID-19 pandemic. Transport Policy 101, 81–88. https://doi.org/10.1016/j.tranpol.2020.12.004

Dzinamarira, T., Murewanhema, G., Musuka, G., 2022. Different SARS-CoV-2 variants, same prevention strategies. Public Health Pract (Oxf) 3, 100223. https://doi.org/10.1016/j.puhip.2021.100223

Mesgarpour, M., Abad, J.M.N., Alizadeh, R., Wongwises, S., Doranehgard, M.H., Jowkar, S., Karimi, N., 2022. Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – A machine learning approach. Chemical Engineering Journal 430, 132761. https://doi.org/10.1016/j.cej.2021.132761

Subbarao, S.S.V., Kadali, R., 2021. Impact of COVID-19 pandemic lockdown on the public transportation system and strategic plans to improve PT ridership: a review. Innov. Infrastruct. Solut. 7, 97. https://doi.org/10.1007/s41062-021-00693-9

 

R#2 – Comment#3: What are the limitations/drawbacks of the related literature studies? What is missing in the literature that would this study fill in?

R#2 – Answer#3:  To the best of our knowledge, most studies were carried out in other contexts and perceptions, cultural issues are different, as is the dependence and quality of the public transport system. Also, we emphasize that understanding the impact, barriers and solutions in other contexts is fundamental, mainly because the resources destined to public transport are scarce. As the review can see in the picture below (Figure 1) most of the studies currently available are based on data from the global north. We produced the imagen in the VosViewer after search for paper on Scopus database using as key words “public”, “transport” and  “covid”. We only find 6 studies in Brazil focusing on public transportation and COVID-19 (1.2% of the total number of papers identifies). Even tough, none of the studies proposed the approach that we are using in this study.

 

R#2 – Comment#4: Consequently, what are the main contributions of this study? What is unique about this paper with respect to the current studies? The research gap in the literature is unclear.

R#2 – Comment#5: Research questions, that drive the paper, should be built in the introduction from an ongoing and pertinent bibliography (up to 2022). Identifying a research gap is a must; key is showing its significance to the field.

R#2 – Answer#4-5:  We believe that “R#2 – Answer#1-2” and “R#2 – Answer#3” answer comments 4 and 5.

 

R#2 – Comment#6: Authors did not discuss the other possible approaches/techniques that could have been used and their limitations/drawbacks that made them choose the selected models throughout the manuscript specifically. No justification or evidence provided to justify the selected methods

R#2 – Answer#6: Thank you for your comments. The hybrid choice models are a rational choice to accomplish the study's goal. Beyond the ambition of identifying the relative importance over a set of alternatives for barriers and countermeasure that led to using a hierarchical choice model, the authors wanted to evaluate how pre-concede perception of public transportation affects how users perceived the COVID-19 response. Therefore, it is necessary to construct latent variables to incorporate attitudinal attributes into the utility function. Moreover, the simultaneous estimation process used in the hybrid choice models provides more conscious and rigorous results. We incorporated the explanation above in the manuscript’ text.      

 

R#2 – Comment#7: What does Figure 1 represent? What does a “framework” mean here? Does this show the relation between the different used characteristics and variables? The figure is confusing. What do the oval, circle, rectangle, round-sided rectangle shapes mean? The used notations are ambiguous and not standardized. I suggest restructuring the figure and standardize the representation of the notations with the adequate description and explanation of the nature of this figure.

R#2 – Answer#7: Thank you for the questions and suggestions. The visual representation of the interrelation among variables included in the model is commonly used in hybrid choice models (e.g., Ben-Akiva et al. 2002. Hybrid Choice Models: Progress and Challenges). We had additional features in the figures and a legend to effortlessly understand it. We also increased the notation in the figure and the correspondence with the results table.

 

R#2 – Comment#8: Authors did not provide any information about the experimental environment that has been used to evaluate their proposed model.

R#2 – Answer#8: Thanks for your comment. We added information about de data collection, methodology, model interpretation and validation in different sections of the paper. We explain about these in Answers#6,7,9,10, 12, 13.      

These ideas may influence how they set up the experiment, how they collect the data, and how they interpret the data.

 

R#2 – Comment#9: How was the experimental evaluation performed? Authors did not clarify the experimental approach and the evaluation methodology that have been followed to provide the different comparison tables presented.

R#2 – Answer#9: Thank you for your question. We include a new paragraph on the method session to explain how we evaluated models performance including an explanation over the load and the robust t-ratio test (see below). We also included the final log-likelihood and the Akaike information criterion (AIC). Please note that we do not aim to compare the three models since they represent different characteristics that could positively or negatively affect the individuals' perception of safety during the COVID-19 outbreak.

“The results are evaluated based on significance of the loads (. We test significance using the robust t     -ratio values. T-ratios are calculated dividing the load per the standard errors. Values over 1.96 indicated the load is      statistically significant at 95% confidence level. The log-likelihood and the Akaike information criterion (AIC) are presented to compare the model in different contexts further.” 

 

R#2 – Comment#10: Authors did not specify on what basis they selected the experimental setting values.

R#2 – Answer#10: As we explain below, in Answer #14 (limitations of the work)      , due to      the impossibility to conduct face-to-face interviews during pandemic, we conducted web surveys. Web-questionnaires may produce bias due to self-selection. In order to address this issue, we considered the distribution of the respondents during data collection, in order to keep the same trend that observed in Porto Alegre. The distribution of respondents by age and gender do not match exactly that observed in the population census, but it follows a similar trend.

The distribution of respondents by modal split follows a similar trend that observed in the Porto Alegre

We clarified this on page 11 (final paragraph): “The distribution of respondents by modal split follows a similar trend that observed in the Porto Alegre”

 

R#2 – Comment#11: Answer your research question in the conclusions; what did we learn compared with current, significant research (up to 2022)?

R#2 – Answer#11: Thank you for the suggestion. The conclusion text was re-written to explicitly respond to the research questions presented in the last paragraph of the introduction session. After each answer, the authors highlighted the theoretical and practical contributions of the research finds.

 

R#2 – Comment#12: How can the outcomes of this study be measured? How can we quantify the results of this study in terms of numbers and percentages? How can we judge - without numbers - that this study adds a value to the literature? Authors should avoid using fuzzy wordings and use instead numbers and percentages to quantify their findings. Quantifying the outcomes should be represented clearly in the Abstract, Results Discussion and the Conclusion sections.

R#2 – Answer#12: Thank you for your comments and suggestions to upgrade the quality of the work we presented. The results have high practicability for policymakers, transport planners, and public transport operators since they provide insights about the COVID-19 response measures perceived as safer. Eliminating barriers and applying measures following users' perceptions might avoid the migration of public transportation users to other transport modes due to the fear of COVID-19 contamination. Moreover, the loads for each barrier/countermeasure are the model's constants and should be keep to correctly predict market shares. Combining observed data for each barrier/contrameasure with the weight obtained for each one, it could be possible to create indicators and compare them with other regions where the survey has been applied.      A discussion over this topic was added in the future work paragraph. 

We reviewed the text to simplify the language used specially regarding the attributes      alternative tested in the study.      Additionally, Abstract, Results Discussion and the Conclusion were reviewed to clearly present the study outcomes.

 

R#2 – Comment#13: Authors should add a section to discuss the threats to validity of their work, in terms of the internal, external, statistical, and construct validity.

R#2 – Comment#14: Internal validity of the proposed model should address the basis on which the parameters were determined to evaluate the proposed work.

R#2 – Answer#13-14: Thank you for your suggestion. As we mentioned before, we include a new paragraph on the method session to explain how we evaluated models performance including an explanation over the load and the robust t-ratio test (see below). We also included the final log-likelihood and the Akaike information criterion (AIC). Please note that we do not aim to compare the three models since they represent different characteristics that could positively or negatively affect the individuals' perception of safety during the COVID-19 outbreak.

“The results are evaluated based on significance of the loads (γ,ζ,λ,δ,β). We test significance using the robust t.-ratio values. T-ratios are calculated dividing the load per the standard errors. Values over 1.96 indicated the load is      statistically significant at 95% confidence level. The log-likelihood and the Akaike information criterion (AIC) are presented to compare the model in different contexts further.”       

 

R#2 – Comment#15: What are the limitations of your work?

R#2 – Answer#15:Considering the impossibility to conduct face-to-face interviews during pandemic, we conducted web surveys. Face-to-face surveys probably deliver more representative results. In order to address this issue, we considered the distribution of the respondents during data collection, in order to keep the same trend that observed in Porto Alegre. The distribution of respondents by age and gender do not match exactly that observed in the population census, but it follows a similar trend.    

The distribution of respondents by modal split follows a similar trend that observed in the Porto Alegre . We      clarified this on page 11 (final paragraph): “The distribution of respondents by modal split follows a similar trend that observed in the Porto Alegre

    

R#2 – Comment#16: No future work is provided.

R#2 – Answer#16:      A longitudinal survey could be conducted in future works, to analyze how preferences and attitudes vary over time. Still, studies can be carried out in other contexts to cross-region comparison. We included a paragraph exploring the possibilities for future work at the end of the final remarks.

 

R#2 – Comment#17: Overall, a serious enhancement is required through a clarified detailed motivation, problem definition, real research gap, experimentation, justified claims and approaches, deep analysis, quantified discussions and comparisons with current, significant research (up to 2022).

R#2 – Answer#17: Thank you for your comments. We clarified all these aspects in the paper as the reviewer suggested.

Author Response File: Author Response.docx