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Article
Peer-Review Record

Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety

Sustainability 2019, 11(11), 3142; https://doi.org/10.3390/su11113142
by Danish Farooq *, Sarbast Moslem and Szabolcs Duleba
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Sustainability 2019, 11(11), 3142; https://doi.org/10.3390/su11113142
Submission received: 5 May 2019 / Revised: 29 May 2019 / Accepted: 30 May 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

Round 1

Reviewer 1 Report

This manuscript deals with the evaluation of driver behavior criteria for evolution of sustainable traffic safety. It is a good quality manuscript.

Need some improvements before publication. Some suggestions to improve are.

1.Introduction:

-Convert this paragraph into previous application of Analytical Hierarchy Process (AHP) method in traffic safety and behaviour. (Line 52-63)….also discuss about the research gap with respect to application of already used variables etc.

-Add discussion about comparative status of AHP based on existing research with reference to other applied techniques (4-5 other techniques) for similar purpose.

Driving on urban roads requires full attention because of the complex driving operations that need a critical decision-making process in order to identify the risk factors for road safety. Human behavior is considered as the most critical factor that affect road safety. The present study demonstrates an integrated model to highlight the most critical driver behavior factors related to road safety, the presented model is comprised of the Analytical Hierarchy Process (AHP) method along with Kendall’s coefficient of concordance correlation method. To better understand the significance of the driver behavior for sustainable traffic safety, the study analyzed twenty hypothetical most critical factors. A comprehensive AHP approach was employed to assign weights to each examined factor and enumerate the relative importance of each factor. For aggregation, the geometric mean technique was conducted to get a general assessment for all groups on perceived road issues. Further, Kendall’s coefficient of concordance was employed to estimate the agreement degree among evaluator groups for each level. The study recommends that the high ranked risky driver behavior factors should be more focused to solve road safety issues.

2.Materials and Methods

As stated in the Introduction, in our research, the AHP approach was applied based on the designed hierarchical structure for estimation of examined driver behavior factors affecting road safety. The primal step of the methodology was to construct a hierarchy structure for the driver behavior criteria and sub-criteria. Line 91-92... (English revision required)

4. Conclusions

Add a subheading in discussion of result section…Advantages and limitations of using AHP in road safety (number it 4.Heading)


Author Response

You certainly kindly spent your valuable time on this manuscript, and we are grateful for the constructive comments and the detailed suggestions.


1.Introduction:

-Convert this paragraph into previous application of Analytical Hierarchy Process (AHP) method in traffic safety and behaviour. Line 52-63)…. (also discuss about the research gap with respect to application of already used variables etc.


Thank you for your suggestion. We converted this paragraph accordingly. Also, we added text about the research gap with respect to the application of already used variables.


Add discussion about comparative status of AHP based on existing research with reference to other applied techniques (4-5 other techniques) for similar purpose.


We discussed the comparative status of AHP based on existing research with reference to other applications used for a similar purpose like to study driver behavior related to road safety.


Driving on urban roads requires full attention because of the complex driving operations that need a critical decision-making process in order to identify the risk factors for road safety. Human behavior is considered as the most critical factor that affects road safety. The present study demonstrates an integrated model to highlight the most critical driver behavior factors related to road safety, the presented model is comprised of the Analytical Hierarchy Process (AHP) method along with Kendall’s coefficient of concordance correlation method. To better understand the significance of the driver behavior for sustainable traffic safety, the study analyzed twenty hypothetical most critical factors. A comprehensive AHP approach was employed to assign weights to each examined factor and enumerate the relative importance of each factor. For aggregation, the geometric mean technique was conducted to get a general assessment for all groups on perceived road issues. Further, Kendall’s coefficient of concordance was employed to estimate the agreement degree among evaluator groups for each level. The study recommends that the high ranked risky driver behavior factors should be more focused to solve road safety issues.


We agree with the Reviewer and amended the text in the article based on this comment.


2.Materials and Methods

As stated in the Introduction, in our research, the AHP approach was applied based on the designed hierarchical structure for estimation of examined driver behavior factors affecting road safety. The primal step of the methodology was to construct a hierarchy structure for the driver behavior criteria and sub-criteria. …Line 91-92... (English revision required)


Thank you for your valuable comment, the text was modified.


4. Conclusions 

Add a subheading in discussion of result section…Advantages and limitations of using AHP in road safety (number it 4.Heading)


The requested additions have been done.


Finally, we appreciate your support and your constructive suggestions. We hope that you will find this revised version worthwhile to get published. 

Thank you very much again.

 The authors


Author Response File: Author Response.docx

Reviewer 2 Report

The paper presents a multicriteria method for ranking and prioritizing the critical driver behavior criteria without applying statistical data analysis.  The subject is very actual and he paper is interesting, but sometimes it is difficult to read.

In table 2, It is not clear what the numbers 1 and 0 mean. Does it mean that there are only males (no females) in group A and only job (?) (no students) in group B? If this is true are the groups suitable for the analysis? why?

Why table 3 is 6x6?

line 179: are the authors sure that they have four (3x3) matrices? Aren't they three (3x3) matrices and one (4x4) matrix?

line 255:where is the evidence that "The results showed that “Lapses” was the most important criteria followed by “Errors”".

line 256: the authors talk about "Qatari drivers" and drivers from the other three countries. They had not explained till this point that they have considered foreigns from different countries. Is it important? And is it statistical reliable to distingue different countries if there are only 35 people in the group?

In chapter 3 "Results", it is diffucult to follow the comment regarding tables 10-15. The authors could add the name of the variables in the tables  or they could add the symbol , in brackets, of each variable in the text.

Author Response

We appreciate very much of your positive statements regarding our manuscript. You certainly kindly spent your time on this manuscript, and we are grateful for the constructive comments and the detailed suggestions.



In table 2, It is not clear what the numbers 1 and 0 mean. Does it mean that there are only males (no females) in group A and only job (?) (no students) in group B? If this is true are the groups suitable for the analysis? why?

The numbers in Table 2 has same meaning as Reviewer understood. However, we would like to explain further:

(1) As Group A contained foreign divers which were selected randomly and mean value 1 showed that they are all males (2) As Group B contained experienced drivers and mean value 1 showed that they all have jobs. The study mainly focusing on driving experience of evaluator groups in Hungary irrespective of their gender and job. However, we provided the detail information about the evaluator groups demographic characteristics for readers to understand the paper comprehensively.


Why table 3 is 6x6?

We presented the structure of (6×6) consistent theoretical PC matrices because it is the biggest matrix in the hierarchical structure.


line 179: are the authors sure that they have four (3x3) matrices? Aren't they three (3x3) matrices and one (4x4) matrix?


We are sure that we have four (3x3) matrices: one (3x3) matrix in level 1 + 2 (3x3) matrices in level 2 + one (3x3) matrix in level 3 = 4 (3x3) matrices

However, we do not have any (4x4) matrix in the constructed hierarchical structure.

line 255: where is the evidence that "The results showed that “Lapses” was the most important criteria followed by “Errors”".


The evidence is clear from Table 10. Lapses (C2) is ranked one as the most important factor reported by two groups (B and C) “where its weight scores are higher than the other factors”, followed by Errors (C3) which is ranked second.



line 256: the authors talk about "Qatari drivers" and drivers from the other three countries. They had not explained till this point that they have considered foreigns from different countries. Is it important? And is it statistical reliable to distingue different countries if there are only 35 people in the group?


We justified our results from previous study which also observed “Lapses” as the most significant factor. However, we agree with Reviewer comment about inclusion of confusing words “other three countries” which we removed now in Line 257.

We did not distingue foreign drivers by their countries in our study. We focused mainly on foreign drivers driving experience in Hungary.


In chapter 3 "Results", it is difficult to follow the comment regarding tables 10-15. The authors could add the name of the variables in the tables or they could add the symbol, in brackets, of each variable in the text.


The names of some factors are long, that is the reason of using variables symbols instead of names which are explained in Table 1. However, we added symbols while discussing about each variable in text for readers to easily understand.

Finally, we appreciate your support and your constructive suggestions. We hope that you will find this revised version worthwhile to get published. 

Thank you very much again.

 The authors


Author Response File: Author Response.docx

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