Ranking Road Sections Based on MCDM Model: New Improved Fuzzy SWARA (IMF SWARA)

Round 1

Reviewer 1 Report

Paper deals with topical task. It has clear motivation and great practical value.

It is technically sound and has all nesessary sections.

Proposed approach is logical, results are clear.

I have only one suggestions, please add main contribution in the end of the Introduction section.

Author Response

Reviewer 1:

Thank you very much for the positive review.

Paper deals with topical task. It has clear motivation and great practical value.

It is technically sound and has all nesessary sections.

Proposed approach is logical, results are clear.

I have only one suggestions, please add main contribution in the end of the Introduction section.

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Comment 1: Please add main contribution in the end of the Introduction section.

Reply: The two ways of contribution are presented in the penultimate paragraph of introduction as follow:

In addition to the importance of the research field and motivation for conducting this study, it is very important to emphasize that in addition to the professional contribution reflected in assessing the safety level of considered sections, a significant scientific contribution was made too. It is reflected through forming an integrated fuzzy Multi-Criteria Decision-Making (MCDM) model with an emphasis on defining the IMF SWARA method that eliminates the shortcomings of the previously developed fuzzy SWARA method, which is explained in detail in the Materials and Methods section. It is also important to emphasize that Dombi and Bonferroni aggregators were used to average specific values of inputs and outputs.

Reviewer 2 Report

The paper attempts to use integrated MCDM method for ranking road sections. The topic is interesting, but current version of the paper is not well organized. Some major revisions are needed. My detailed comments can be found in the attachment.

Comments for author File: Comments.pdf

Author Response

Reviewer 2:

Thank you very much for the useful suggestions. We accepted all of the suggestions and we are sure that this will improve the quality and contribute to a better understanding of the paper.

 

The paper attempts to use integrated MCDM method for ranking road sections. The topic is interesting, but current version of the paper is not well organized. Some major revisions are needed.

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Comment 1: For the title, there are some grammar mistakes. Moreover, the paper used many MCDM method. However, only two MCDM methods are mentioned in the title. Is it appropriate? Please use a title that can summarize the main work of the paper.

Reply: Yes, you have right, but the main focus of the paper is on two methods: Improved Fuzzy SWARA and Fuzzy MARCOS methods, while the rest of the methods have been used in order to verify our original model. Beside this explanation we have changed the title of the paper. Now is „Ranking road sections based on MCDM model: new Improved fuzzy SWARA (IMF SWARA)“

Comment 2: Why the Improved fuzzy SWARA is called SWARA – IF? It seems more like a version of intuitionistic fuzzy SWARA.

Reply: Good observation. The abbreviation of intuitionistic fuzzy SWARA is IF SWARA, while we have called our method Improved Fuzzy SWARA (SWARA IF). For reason that some future readers can mix the two expressions, we have decided to change the name of our method. We have changed from SWARA – IF into IMF SWARA.

Comment 3: The motivation of the paper is unclear. The paper used many different MCDM methods for ranking road sections. Why do you select these methods. If we combine different methods, we can write more and more papers. Please justify the motivation of the paper.

Reply: Yes, it is true. The paper used many different MCDM methods, but as we have mentioned previously the main focus is on the Improved fuzzy SWARA and Fuzzy MARCOS methods. Each high-quality paper should have sensitivity analysis and comparative analysis in order to verify the proposed model and to determine its stability. Each used method has its advantages as has been shown in the part of the methodology section. Motivation can be expressed through the following:

In addition to the importance of the research field and motivation for conducting this study, it is very important to emphasize that in addition to the professional contribution reflected in assessing the safety level of considered sections, a significant scientific contribution was made too. It is reflected through forming an integrated fuzzy Multi-Criteria Decision-Making (MCDM) model with an emphasis on defining the IMF SWARA method that eliminates the shortcomings of the previously developed fuzzy SWARA method, which is explained in detail in the Materials and Methods section. It is also important to emphasize that Dombi and Bonferroni aggregators were used to average specific values of inputs and outputs.

Comment 4: For literature review, the authors should provide some reviews about MCDM methods. For instance, the following papers may be included in the paper: Consensus reaching for group decision making with multi-granular unbalanced linguistic information: A bounded confidence and minimum adjustment-based approach; Consensus reaching for social network group decision making by considering leadership and bounded confidence. Moreover, besides listing papers, some comments and summarization should be provided in the literature review section.

Reply: Thank you for your suggestion. We found that mentioned references are more appropriate for conclusion section in part where we have described guidelines for future research. Besides, we have added few more references related to MCDM in literature review section. New added text in Literature review section is as follow:

Implementation of different approaches in one integrated model is a practice that very often ensures more accurate results. Such models are preferable, so many researchers bring new ideas and implement them in the field of transport and traffic. For example the approach based on an individual DEA for determining the efficiency of 197 municipalities contains two inputs and 14 outputs have been applied in [21] applied. The obtained results showed that due to the weights of the input, it is possible for a more efficient municipality to be ranked lower. In the study [22] has been applied the Analytic Hierarchy Process (AHP) method for determining the influence of traffic factor interaction on the rate of traffic accidents. The MCDM model was also applied in [23] for the identification of priority black spots in order to decrease the risk in traffic, while in [24] authors have implemented the AHP method in order to evaluate and rank road section design. In research [25] has been created a new multi-criteria and simultaneous multi-objective optimization (MOO) model using the AHP method for evaluating and ranking traffic and geometric elements.

For transport projects, policies, or policy measures, various multi-criteria methods have been used. The study [26] defines why MCDM models play so important roles while dealing with various categories of decision problems that arise in mass transit systems. Also, the importance of MCDM models in this field has been proven with two case studies. In the study [27], a novel hybrid model which combines the fuzzy step-wise weight assessment ratio analysis (FSWARA) and the fuzzy best-worst method (FBWM) is developed for selection equipment in container terminal. By applying MCDM, quality of life in urban environments was assessed at three spatial levels (socioeconomic, environmental, and accessibility) [28]. For transport projects, policies or policy measures evaluation, various multi-criteria methods have been developed and effectively applied to complement conventional cost effectiveness and cost benefit analysis from 1982 to 2019 [29]. Determination transportation and traffic risk is very popular and interesting activity that can help participants to avoid some risk and conflict situations. The authors in [30] have applied fuzzy PIvot Pairwise RElative Criteria Importance Assessment (Fuzzy PIPRECIA) method to determine and rank the road transportation risk factors in Giresun province.

In total we have added 17 new references to the paper.

Comment 5: Please discuss the merits and limitations of your study.

Reply: We have added in the conclusion: „Limitations of this study can be manifested through a small number of considered road sections and including input/output parameters without performed principal component analysis“. Merits are as follow:

The greatest contributions of this research can be observed from two aspects: scientific and professional. From a scientific aspect, the main, and probably the most important contribution is the development of the Improved fuzzy SWARA method (IMF SWARA), which involves overcoming the disadvantages of the fuzzy SWARA method through the following:

1) Using the fuzzy SWARA method, it is impossible to obtain results in which two criteria have equal fuzzy weights. By applying the Improved fuzzy SWARA method, two or more criteria can have equal values.

2) On the contrary, applying the inadequate TFN scale shown in Table 2, when decision-makers indicate that two criteria have the same value by assigning TFN (1,1,1), the criterion Cj in relation to Cj-1 gets a value that is twice less than Cj. By applying the Improved fuzzy SWARA method, assigning the value (0,0,0) it is really obtained equal values, but not twice as large.

3) By increasing the number of criteria in the model, the least significant criteria get values that can be negligible, i.e. with a tendency to zero. By applying the Improved fuzzy SWARA method, less significant criteria have higher values, and can play a greater role in a decision-making process.

Then, the integration of the developed IMF SWARA method with the fuzzy MARCOS method was performed, which is also a contribution of the paper. In order to achieve greater precision in the creation of input data, the originality of the given model was manifested by the application of two aggregators: Dombi and Bonferroni, which were used to average the input-output parameters. The creation and verification of such a model certainly represents a contribution to the overall literature that considers multi-criteria problems. If we take into account that according to Salabun et. al. [50] in many studies, the problem of selecting the proper methods and parameters for the decision problems is raised we can conclude that our integrated fuzzy model represents a good solution. This has been proved through mentioned scientific contributions of the developed model.

From a professional aspect, the contribution of the research is reflected through the quantification of the safety degree of certain sections of road infrastructure, which is an important parameter in practice. Taking into account the previously stated parameters with a focus on exceeding the speed by passenger cars, the road sections from the considered set that represent risk places have been defined, and a corrective measure in traffic management is necessary.

 

 

Comment 6: Discuss more about your future studies, for instance, you may consider the situations when decision makers have self-confidence over their preference by referring to Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making.

Reply: We have cited this reference. Also, future research directions have extended as follow:

Future research may need to be conducted on a number of road sections with the possibility of implementing the proposed model. The existing model, with the increase of input parameters, could contribute to a much more selective level of choosing the rank of road sections. Can be added the number of access points on each section, the radius of the road, etc. as input parameters. Also, by applying the existing model through observing other input parameters, it would be obtained an extensive analysis in ranking the safety level of two-lane roads in terms of multi-criteria traffic analysis. Besides, can be implemented PCA - DEA analysis [51] to determine efficiency of considered road sections or implement group MCDM model [52] with some different uncertauinty theories such multi-granular unbalanced linguistic information [53], intuitionistic 2-tuple linguistic sets [54] or grey theory [55]. Considering the approach presented in [56] can be useful in developing future integrated decision-making models.

Comment 7: The layout of the equations and figures should be significantly improved.

Reply: We have corrected laqout of Figures, but please note that equations and the whole paper are prepared according to the official journal template.

Comment 8: The language of the paper MUST be improved with the help of a native

Reply: Thank you, the paper will be improved with the help of native in case that paper will be accepted.

Reviewer 3 Report

The authors have developed an integrated fuzzy model for ranking road sections based on four inputs and four outputs. The main objective of the work is to determine the safety degree of observed road sections by the methodology developed. The most significant contribution of the paper is reflected in developing the Improved fuzzy Step-wise Weight Assessment Ratio Analysis method and integration with the fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS) method. In my opinion, it is a well written scientific work. However, some shortcomings must be eliminated before accepting. The list of my comments is as follows:
1. In introduction please emphasis more clear contribution of work. Additionally some aspects of background should be extended, e.g.,  Supplier selection for steel making company by using combined Grey-MARCOS methods; Sustainable supplier selection using combined FUCOM – Rough SAW model; or similar
2. Also motivation why the authors using these methods must be stronger emphasis. There can be helpful works like: Generalised framework for multi-criteria method selection; Are mcda methods benchmarkable? a comparative study of topsis, vikor, copras, and promethee ii methods; or similar
3. In line 436 please use dots instead stars.
4. The analysis using WS and rw coefficients are missing. It is a interesting a new coefficient of rankings similarity in decision-making problem, which can increase the contribution of this paper. 
5. In the literature review should be presented current research trends, e.g., doi.org/10.1016/j.eswa.2021.115088; and doi.org/10.31181/dmame2104127b; or similar
6. The further research directions should be extended.

Author Response

Reviewer 3:

Thank you very much for the useful suggestions. We accepted all of the suggestions and we are sure that this will improve the quality and contribute to a better understanding of the paper.

 

The authors have developed an integrated fuzzy model for ranking road sections based on four inputs and four outputs. The main objective of the work is to determine the safety degree of observed road sections by the methodology developed. The most significant contribution of the paper is reflected in developing the Improved fuzzy Step-wise Weight Assessment Ratio Analysis method and integration with the fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS) method. In my opinion, it is a well written scientific work. However, some shortcomings must be eliminated before accepting.

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The list of my comments is as follows

Comment 1: In introduction please emphasis more clear contribution of work. Additionally some aspects of background should be extended, e.g.,  Supplier selection for steel making company by using combined Grey-MARCOS methods; Sustainable supplier selection using combined FUCOM – Rough SAW model; or similar.

Reply: The two ways of contribution are presented in the penultimate paragraph of introduction as follow:

In addition to the importance of the research field and motivation for conducting this study, it is very important to emphasize that in addition to the professional contribution reflected in assessing the safety level of considered sections, a significant scientific contribution was made too. It is reflected through forming an integrated fuzzy Multi-Criteria Decision-Making (MCDM) model with an emphasis on defining the IMF SWARA method that eliminates the shortcomings of the previously developed fuzzy SWARA method, which is explained in detail in the Materials and Methods section. It is also important to emphasize that Dombi and Bonferroni aggregators were used to average specific values of inputs and outputs. The first proposed references are cited in the conclusion.

Comment 2: Also motivation why the authors using these methods must be stronger emphasis. There can be helpful works like: Generalised framework for multi-criteria method selection; Are mcda methods benchmarkable? a comparative study of topsis, vikor, copras, and promethee ii methods; or similar.

Reply: Thank you for the suggestion. We have added this reference. Also, please see the conclusion section for a detailed explanation of the used methods.

Comment 3: In line 436 please use dots instead stars.

Reply: Thank you. Corrected.

Comment 4: The analysis using WS and rw coefficients are missing. It is a interesting a new coefficient of rankings similarity in decision-making problem, which can increase the contribution of this paper.

Reply: Thank you for your suggestion. We are well known with this really impressive coefficient of ranking similarity. In some of our previous studies we have used it, but in this research isn't a need for that, because in the whole sensitivity analysis including changing criteria weights and comparative analysis no changes in rank. All obtained results are in full correlation.

Comment 5: In the literature review should be presented current research trends, e.g., doi.org/10.1016/j.eswa.2021.115088; and doi.org/10.31181/dmame2104127b; or similar.

Reply: Thank you for your observation. We have added reference doi.org/10.1016/j.eswa.2021.115088, but this reference doi.org/10.31181/dmame2104127b was in the original version of the paper.

Comment 6: The further research directions should be extended.

Reply: We have extended guidelines for future research.

Future research may need to be conducted on a number of road sections with the possibility of implementing the proposed model. The existing model, with the increase of input parameters, could contribute to a much more selective level of choosing the rank of road sections. Can be added the number of access points on each section, the radius of the road, etc. as input parameters. Also, by applying the existing model through observing other input parameters, it would be obtained an extensive analysis in ranking the safety level of two-lane roads in terms of multi-criteria traffic analysis. Besides, can be implemented PCA - DEA analysis [51] to determine efficiency of considered road sections or implement group MCDM model [52] with some different uncertauinty theories such multi-granular unbalanced linguistic information [53], intuitionistic 2-tuple linguistic sets [54] or grey theory [55]. Considering the approach presented in [56] can be useful in developing future integrated decision-making models.

Round 2

Reviewer 2 Report

The paper has been revised and can be accepted for publication.

Reviewer 3 Report

The authors improved their work. It is valuable scientific work that I highly recommend accepting in the current form.