Driver’s Perceived Satisfaction at Urban Roundabouts—A Structural Equation-Modeling Approach

Round 1

Reviewer 1 Report

The subject is actual due to the present increasing of road traffic. Studies on different subjects related to increasing of safety on the roads are welcome and can generate solutions for better traffic arrangements in the future.

Is necessary a correction on second page, line 51, regarding a reference source not founded.

Some of the tables need to be arranged, like Table 5, to be place completely on one page, easily to be understood and analysed.

There are two of Table 7. If the second one is Table 8, means, the last table is no.9.

Author Response

We appreciate the time and effort that you and the reviewers dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated most of the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns. All page numbers refer to the revised manuscript file.

Author Response File: Author Response.pdf

Reviewer 2 Report

 

The manuscript focuses on an important and largely neglected aspect of road infrastructures, that is the drivers’ satisfaction for urban roundabouts.  The paper is mostly well-written, especially in its introductory section, and the employed methodology is overall consistent with the aims of the study. However, the report suffers from some shortcomings that should be addressed, as detailed below (mix of minor and major revisions).

 Abstract

·      -  Please, declare the meaning of HCM and HCQS before using the acronyms.

Introduction

·    -  Line 38: why do the references start from n. 13? I think there has been some problem with the order of the references (see also line 51).

Method

·   -   How were the 25 items selected and formulated? Did you perform a literature review and select some items from previous questionnaires? Note that I am referring to the single items and not to the sections. Please, give more details regarding the construction of the questionnaire.

·  -   How was distributed the questionnaire? “Online” may indicate lots of different channels.

·   -   Please, include a table to summarize the demographic characteristics of the final sample (descriptive statistics).

·    -   Were some responses discarded? If yes, on which basis? In which percentage?

·    -    Did you also collect information regarding the respondents’ annual mileage? If not, please provide justification.

·   -   Why the bicyclists and the pedestrians were not discarded from the analysis? If you want to focus on the car drivers (which is correct), you should use a criterion to eliminate respondents without enough driving experience. I suggest discarding these data and re-run the analyses to confirm them.

·    -    Please, declare the statistical software and packages (included the version) used for all the analyses.

·    -   The idea to use a factorial analysis followed by SEM seems very good to me. However, there are some issues in the performed factorial analysis that needs to be resolved. 1) Why the authors decided to use a 6-factors solution when the results (both the scree plot and the percentage of explained variance) clearly indicate that the best solution is a 3-factors solution? Indeed, the 6-factors solution leads to the identification of three components with only one item, which is quite rare and frankly not recommendable. 2) Why a confirmation factorial analysis was not run, but the authors directly went to the SEM analysis? I suggest using a confirmation factorial analysis to verify whether the 6-factor solution is appropriate or not. I also suggest running another explorative factorial analysis using a stop criterion (e.g., 4 factors) and to see how the components and the items' distribution change. 3) Once identified the best factorial structure, please, re-run the following analyses (SEM etc.) accordingly.

Discussion

·    -  Given the presence of several steps in the analyses, I would suggest dividing the Discussion section accordingly, to facilitate the understanding and the logic behind each step and the corresponding results.

Author Response

We appreciate the time and effort that you and the reviewers dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated most of the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns. All page numbers refer to the revised manuscript file.

Author Response File: Author Response.pdf

Reviewer 3 Report

In this work the authors address the topic of developing an efficient approach to measuring the quality of service (QOS) perceived by car drivers at urban roundabouts.

The objectives are clear and well connected to the state of the art. The description of the problem is pretty accurate and well-structured. Furthermore, the bibliographic references are appropriate and English is fluent and clear.

However, further explanations and details are requested to ensure a complete understanding of the article, in particular:

1)What do you mean with the variable “landscape”   

2)[196-197] “Endogenous variables are equivalent to dependent variables and are equal to the independent variable, [33,35).” It’s not clear to me. (also a typing error)

3)[280] “Another aspect of the specific component is luck of uniformity in terms of priority rules”. It’s not clear to me

4)[299] “The last factor is not considered as very important, maybe due to the fact that roundabouts in Greece are lucking of distinguished features and/or planting zones” What do you mean?

5) [380] “However, further studies are necessary in order to better understand influence of human factors and local conditions to driving behavior”.  In my opinion this is too general, it should be more specific and detailed.  

6)Suggestions: What about including the perception of pedestrians and cyclists in further studies?

Author Response

We appreciate the time and effort that you and the reviewers dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated most of the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns. All page numbers refer to the revised manuscript file.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The new version of the manuscript has for sure increased the overall clarity of the work, providing crucial details for the understanding of its aims and methods.

However, I still have some doubts regarding the methodological core of the work, i.e., the appropriateness of a 6-factor solution instead of a 3-factor one: indeed, the authors provided theoretical but not data-drive justification for this choice. Note that the justification provided regarding the increased percentage of variance explained by a 6-factor solution is tautological: using this logic, the most appropriate solution would therefore be a 25-factor solution. In order to warn the reader of the possible limits of this solution, I suggest openly disclosing these aspects in the discussion. Otherwise, more data-driven justification should be provided for the manuscript to be accepted.

Author Response

Thank you for the valuable comments.

The methodological core of the survey is based on a theoretical driven base, supported by an extensive literature review on SEM models derived from an EFA (Exploratory Factor Analysis), justified by data-drive results.

The theoretical component included consideration of the most used criteria to identify the number of factors in an exploratory factor analysis i.e.:

1. Total Variance explained according to a threshold (e.g. 75%)
2. Kaiser-Gutman criterion (Kaiser eigenvalue criterion)
3. Scree plot and its modifications

 

Among these, the Kaiser’s criterion states the proposed number of factors regarding the number of eigenvalues with a value higher than 1. Though, according to literature, the Kaiser’s approach is neither the optimal nor the best according to surveys, and the scree plot is biased due to the subjective judgement (e.g. elbow criterion). In addition, regarding the Kaiser’s criterion, many researchers found that the criterion underestimates (e.g. Cliff, 1988) or overestimates (e.g. Yeomans & Golder, 1982) the number of factors that should be retained. It has also been demonstrated that the number of factors suggested by the Kaiser criterion is dependent on the number of variables, the reliability of the factors or on the MV-to-factor ratio and the range of communalities. Thus, the general conclusion is that there is little justification for using the Kaiser criterion to decide how many factors to retain.

1. Cliff, N. (1988). The eigenvalues-greater-than-one rule and the reliability of components. Psychological Bulletin, 103(2), 276–279. https://doi.org/10.1037/0033-2909.103.2.276
2. Yeomans, K. A., & Golder, P. A. (1982). The Guttman-Kaiser Criterion as a Predictor of the Number of Common Factors. Journal of the Royal Statistical Society. Series D (The Statistician), 31(3), 221–229. https://doi.org/10.2307/2987988

 

Based on the above remarks, in the current survey, the total variance criterion was considered as the optimum criterion, used with a threshold of 75%. In addition to the above explanation (and also due to the low 55.955% of the total variance explanation from a 3-factor model), each of the factors “Congestion”, “Road pavement quality” and “Landscaping”, present low correlation levels with the rest of the factors of the survey, while the remaining ones, appear to have a stronger intercorellation (resulting to the formation of the first 3 components).

More attempts were considered with the selection of 4 and 5 factors for the model. The results were similar to the ones derived with 3 factor analysis, meaning that the total variance criterion was still lower than 70% (2/3 of the total sample).

As a result, considering a 6-factor model was considered. The results of the EFA-PCA method were tested with Equamax rotation method and Anderson-Rubin score estimation method) creating basically new individual components (due to their high scores in each respective component), we reduced the bias obtained from a 3, 4 and 5 factor models. This result is strengthened from the fact that in a 3, 4 or 5 the factors “Congestion”, “Road pavement quality” and “Landscaping” would belong to components with other factors to which they have low correlation levels and negative components resulting to low internal-consistency levels, (Table 4).

The writers strongly believe a high score of the total variance criterion, i.e. the interactions between the examined items, suits best the structural formation of the final equation.

 

 

 

Author Response File: Author Response.docx