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Article
Peer-Review Record

Estimation of Public Charging Demand Using Cellphone Data and Points of Interest-Based Segmentation

World Electr. Veh. J. 2023, 14(2), 35; https://doi.org/10.3390/wevj14020035
by Victor Radermecker 1,† and Lieselot Vanhaverbeke 2,*,†
Reviewer 1: Anonymous
Reviewer 2:
World Electr. Veh. J. 2023, 14(2), 35; https://doi.org/10.3390/wevj14020035
Submission received: 24 November 2022 / Revised: 23 December 2022 / Accepted: 10 January 2023 / Published: 30 January 2023

Round 1

Reviewer 1 Report

This is a very interesting paper with interesting methods researching a very important problem. However, the reviewer has some major concerns that authors need to address. 

1. The introduction is too short. This should be expanded to give a proper background and motivation of the research. The contributions can be included as well. 

2. It is not clear why Cellphone Data could be used to predict EV charging demand? What is the assumption on this? How do you tell the cellphone data are from drivers or from other people (e.g. pedestrians)?

3. It is clear authors make some assumptions and hypothesis about using other information to predict charging demand (e.g. POI segmentation). I do not find details of validation of such assumption. Do you have some dataset with ground truth to validate your approach? 

 

Author Response

Hello,

I hope this message finds you well. Thank you very much for your previous remarks, I really appreciate your time. The following updated version of the article includes all the corrections proposed in your review. 

  1. The introduction is enhanced, now giving additional context to the reader. It mainly focuses on the current challenges and ends by outlining the proposed solution. 
  2. A tabular representation of the segmentation results was added.
  3. A new perspective on future works was added.
  4. The open-source datasets used are described and additional context is given. The various links are all provided to allow anyone to reproduce the study.
  5. Other minor modifications/corrections were changed throughout the text. 

Again, thanks for your remarks which were truly helpful. Wishing you wonderful Christmas and a happy new year 2023. Happy winter holidays!

Best regards,

Victor Radermecker

Reviewer 2 Report

This article proposes an innovative EV charging demand estimation and segmentation method. Some suggestions for improving the manuscript are presented.

1) Introduction section is not enough. The problem definition should be emphasized more. In addition, a short summary of the study without the results should be added as a paragraph. Section 2.3 can be moved to the introduction.

2) It will be more understandable if the segmentation results are presented in tabular form.

3) A perspective for future work should be presented in the Conclusion section.

4) A separate heading describing the dataset should be added in Material and Methods.

Author Response

Hello,

I hope this message finds you well. Thank you very much for your previous remarks, I really appreciate your time. The following updated version of the article includes all the corrections proposed in your review. 

  1. The introduction is enhanced, now giving additional context to the reader. It mainly focuses on the current challenges and ends by outlining the proposed solution. 
  2. A tabular representation of the segmentation results was added.
  3. A new perspective on future works was added.
  4. The open-source datasets used are described and additional context is given. The various links are all provided to allow anyone to reproduce the study.
  5. Other minor modifications/corrections were changed throughout the text. 

Again, thanks for your remarks which were truly helpful. Wishing you wonderful Christmas and a happy new year 2023. Happy winter holidays!

Best regards,

Victor Radermecker

Round 2

Reviewer 1 Report

The authors have addressed my comments. 

Reviewer 2 Report

Necessary revisions have been made. The manuscript is acceptable as it is.

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