Unlocking Trends: Socio-Demographic Insights into Bike Sharing from the 2017 National Household Travel Survey

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

While the paper provides valuable insights into the socio-demographic characteristics of bike-sharing system users, it would be beneficial to further enhance its novelty and contributions. I recommend extending the manuscript by investigating deeper into the statistical analysis, thereby providing a more comprehensive understanding of the relationships explored. Elaborating on the statistical methodologies employed and conducting more analyses could strengthen the paper's impact and contribute significantly to the existing literature on this topic. The manuscript needs a lot of work before it considered for publication. Here are my other remarks:

 

1.      It is highly recommended to enhance the literature review by including additional recent sources, with a focus on publications from 2023 and 2024, to ensure the most up-to-date information is considered. Below are some suggestions for relevant references that could be incorporated into the paper.

https://doi.org/10.1016/j.jtrangeo.2023.103588

2.      Please relocate Table 2 from the Results section to the Data section

3.      I have observed that most of the variables are not significant. Thus, the question is whether the negative binomial regression (NBR) good for this analysis or not. Please justify by providing a more thorough explanation of the rationale behind employing NBR and how it aligns with the research objectives. Also, please add more details about it in the methodology section. Here is a closely related article to depend on:

https://doi.org/10.1016/j.aej.2023.06.087

4.      Focusing on only one specific type (sufficiently active or not) and generating more accurate and focused statistical models could enhance the precision and relevance of the findings allowing for more targeted analysis and interpretation of the data.

5.      It is highly recommended to use more statistical analyses for comparisons and better justifications.

 

 

 

 

 

 

Comments on the Quality of English Language

Acceptable

Author Response

From Reviewer 1:

Comments:

• While the paper provides valuable insights into the socio-demographic characteristics of bike-sharing system users, it would be beneficial to further enhance its novelty and contributions. I recommend extending the manuscript by investigating deeper into the statistical analysis, thereby providing a more comprehensive understanding of the relationships explored. Elaborating on the statistical methodologies employed and conducting more analyses could strengthen the paper's impact and contribute significantly to the existing literature on this topic. The manuscript needs a lot of work before it considered for publication. Here are my other remarks:
• Thank you for the comments. I have conducted models for the count of bike trips for all respondents, those who are insufficiently active, and those who are sufficiently active, respectively, and models for the count of bike share program usage for all respondents, those who are insufficiently active, and those who are sufficiently active.
• Weighted t-tests are performed to explore the differences between individuals who reported biking trips and those who engaged in both biking trips and bike share program usage. Please see the specific responses in the following comments.
• It is highly recommended to enhance the literature review by including additional recent sources, with a focus on publications from 2023 and 2024, to ensure the most up-to-date information is considered. Below are some suggestions for relevant references that could be incorporated into the paper. https://doi.org/10.1016/j.jtrangeo.2023.103588
• Thank you for the suggestion. I have cited the following publications.
  • Mohiuddin, H., Fitch-Polse, D.T. and Handy, S.L., 2023. Does bike-share enhance transport equity? Evidence from the Sacramento, California region. Journal of Transport Geography, 109, p.103588.
  • Zhou, J., Li, Z., Dong, S., Sun, J. and Zhang, Y., 2023. Visualization and bibliometric analysis of e-bike studies: A systematic literature review (1976–2023). Transportation research part D: transport and environment, 122, p.103891.
  • Mina, G., Bonadonna, A., Peira, G. and Beltramo, R., 2024. How to improve the attractiveness of e-bikes for consumers: Insights from a systematic review. Journal of Cleaner Production, p.140957.
  • Mohiuddin, H., Fukushige, T., Fitch-Polse, D.T. and Handy, S.L., 2024. Does dockless bike-share influence transit use? Evidence from the Sacramento region. International Journal of Sustainable Transportation, 18(2), pp.146-167.
  • Mohiuddin, H., Fitch-Polse, D.T. and Handy, S.L., 2024. Examining market segmentation to increase bike-share use and enhance equity: The case of the greater Sacramento region. Transport Policy, 145, pp.279-290.
  • Chen, J. and Huang, L., 2024. Causes of transportation inequality: The case of bike sharing in the US. Case Studies on Transport Policy, 16, p.101199.
  • Hosseini, K., Stefaniec, A., O'Mahony, M. and Caulfield, B., 2023. Optimising shared electric mobility hubs: Insights from performance analysis and factors influencing riding demand. Case Studies on Transport Policy, 13, p.101052.
• Please relocate Table 2 from the Results section to the Data section
• I have relocated Table 2 to the Data section.
• I have observed that most of the variables are not significant. Thus, the question is whether the negative binomial regression (NBR) good for this analysis or not. Please justify by providing a more thorough explanation of the rationale behind employing NBR and how it aligns with the research objectives. Also, please add more details about it in the methodology section. Here is a closely related article to depend on: https://doi.org/10.1016/j.aej.2023.06.087
• Thank you for the suggestion. I have added more explanations and cited this publication. Please see page 7, line 261-277.
• Focusing on only one specific type (sufficiently active or not) and generating more accurate and focused statistical models could enhance the precision and relevance of the findings allowing for more targeted analysis and interpretation of the data.
• This study has conducted models for the count of bike trips for all respondents, those who are insufficiently active, and those who are sufficiently active, respectively. Please see Table 4. For the comparison of model results for those who are insufficiently active and those who are sufficiently active, please see page 11, line 369 – page 12, line 403.
• This study has conducted models for the count of bike share program usage for all respondents, those who are insufficiently active, and those who are sufficiently active. Please see Table 5. For the comparison of model results for those who are insufficiently active and those who are sufficiently active, please see page 14, lines 435–452.
• It is highly recommended to use more statistical analyses for comparisons and better justifications.
• Thank you for the suggestion. Weighted t-tests are performed to explore the differences between individuals who reported biking trips and those who engaged in both biking trips and bike share program usage. Please see page 7, line 250-252 and Table 2 p-value column.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper analyses a set of socio-demographic variables regarding utilization of bike-sharing systems. 

The differences between insufficiently active and sufficiently active respondents are not very big, however in some cases statistically relevant. Please make a remark about the quite small differences between these two groups of respondents. Some of your conclusions suggest that the differences are bigger than they really are.

 

Author Response

From Reviewer 2:

Comments:

• This paper analyses a set of socio-demographic variables regarding utilization of bike-sharing systems.
• Thank you for the comments. I have provided point-to-point responses to your comments.
• The differences between insufficiently active and sufficiently active respondents are not very big, however in some cases statistically relevant. Please make a remark about the quite small differences between these two groups of respondents. Some of your conclusions suggest that the differences are bigger than they really are.
• Thank you for the comments. I have added some sentences to emphasize the differences are not substantial. Please see page 8, line 308 and line 314.

Reviewer 3 Report

Comments and Suggestions for Authors

The topic of your paper is interesting; however, it requires some improvements to meet the publishing standards.

1. Consider changing the title of your article, especially the second part, which is not sufficiently attractive.

2. On page 2, line 62 of the manuscript, the following paper should be cited: Hosseini, K., Stefaniec, A., O'Mahony, M., & Caulfield, B. (2023). Optimising shared electric mobility hubs: Insights from performance analysis and factors influencing riding demand. Case Studies on Transport Policy, 13, 101052. https://doi.org/10.1016/j.cstp.2023.101052.

3. The authors should provide a more detailed explanation of their use of negative binomial regression. The following paper may be useful: Hosseini K, Choudhari T, Stefaniec A, O’Mahony M, Caulfield B. E-bike to the future: Scalability, emission-saving, and eco-efficiency assessment of shared electric mobility hubs. Transportation Research Part D: Transport and Environment. https://doi.org/10.1016/j.trd.2024.104275.

4. The authors need to expand their conclusion section to include policy implications. They should present some real-world evidence derived from the results of their study which could assist transport policymakers.

Author Response

From Reviewer 3:

Comments:

• The topic of your paper is interesting; however, it requires some improvements to meet the publishing standards.
• Thank you for the comments. I have provided point-to-point responses to your comments.
• Consider changing the title of your article, especially the second part, which is not sufficiently attractive.
• I have changed the title. Now it reads, “Unlocking Trends: Socio-Demographic Insights into Bike-Sharing from the 2017 National Household Travel Survey.”
• On page 2, line 62 of the manuscript, the following paper should be cited: Hosseini, K., Stefaniec, A., O'Mahony, M., & Caulfield, B. (2023). Optimising shared electric mobility hubs: Insights from performance analysis and factors influencing riding demand. Case Studies on Transport Policy, 13, 101052. https://doi.org/10.1016/j.cstp.2023.101052.
• Thank you for the suggestion. I have cited this publication. Please see reference number 15.
• The authors should provide a more detailed explanation of their use of negative binomial regression. The following paper may be useful: Hosseini K, Choudhari T, Stefaniec A, O’Mahony M, Caulfield B. E-bike to the future: Scalability, emission-saving, and eco-efficiency assessment of shared electric mobility hubs. Transportation Research Part D: Transport and Environment. https://doi.org/10.1016/j.trd.2024.104275.
• Thank you for the suggestion. I have added more explanations and cited this publication. Please see page 7, line 261-277.
• The authors need to expand their conclusion section to include policy implications. They should present some real-world evidence derived from the results of their study which could assist transport policymakers.
• Thank you for the suggestion. I have added some policy implications and evidence. Please see page 17, line 623 – page 18, line 645.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I would like to thank the author for addressing the comments raised.

However, there is only one remark:

Could you please justify the low value of the R2 extracted from the models?

 

Author Response

From Reviewer 1:

Comments:

• Could you please justify the low value of the R2 extracted from the models?
• Thank you for the comments. Bike trips are influenced by a multitude of factors, including personal preferences, environmental conditions, and situational contexts, many of which are not captured by socio-demographic variables alone. Thus, the model may not include all relevant predictors due to data limitations. Factors such as weather conditions, bike availability, infrastructure quality, and safety perceptions are likely to influence bike trip counts but may not be available in the dataset. I have added as one of the limitations. Please see page 17, line 601-607.