Heterogeneity Aware Emission Macroscopic Fundamental Diagram (e-MFD)
Round 1
Reviewer 1 Report
This paper focused on Heterogeneity aware emission macroscopic fundamental
diagram (e-MFD): The subject matter of this manuscript fits the journal's scope, and the information included in the manuscript seems not to have been published in any other publication so far. However, it seems difficult to adequately evaluate the value of this study because the explanation of the significance of the study, the description of the interpretation and usefulness of the results obtained by the analysis, and the explanation of the model are insufficient. I would like to ask the authors to consider responding to the following comments:
(1) Would you explicitly specify the novelty of your work? What progress against the most recent state-of-the-art similar studies was made?
(2) The Introduction section should be improved. It should be dedicated to presenting a critical analysis of state-of-the-art related work to justify the study's objective. In addition, critical comments should be made on the results of the cited works.
(3) The main objective of the work must be written in a more precise and concise way at the end of the introduction section. Please carefully check recent literature and discuss/cite as you see fit, and update your reference list. Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations. A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability. Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time. Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach
(4) A summative table highlighting the outcomes from previous research is expected at the end of the introduction section
(5) There is a room to improve the research methodology for publishing in an international journal. Furthermore, the numerical experiments were insufficient.
(6) The reviewer think some figures related to the computation results should be presented to improve the quality of this paper.
(7) The conclusion section provides a lack of contributions to this manuscript. Provide the key features, merits, and limitations of the proposed approach to clarify the precise boundary of the algorithms. The implication of the proposed method is also required.
(8) This paper is generally well written, but I found multiple typographic and editorial errors over the entire manuscript, including the equations. The authors need to proofread again carefully.
Author Response
Please see the attachment
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper is interesting and well-written to consider congestion distribution as an independent variable for emission estimation modeling.
The authors need to define the meaning of "Heterogeneity aware", which is somehow too technical for the general readers.
Line 305: some texts are missing here "compared to Saedi model (around 10..."
Author Response
Hello
The heterogeneity-aware term means the e-MFD is aware of congestion distribution heterogeneity and takes it into consideration for emission estimation.
Fixed the issues.
Reviewer 3 Report
The authors' e-MFD model has lower standard deviation of the density than previously known models. This is a nice result with adequate support. The new model can be used for emission in realistic cases by importing velocity, density and standard deviation. Very importantly, the model can be used to reduce emissions in hierarchical traffic managers Thus, managers are able to optimize the network to achieve minimum pollution.
Author Response
Thank you
Reviewer 4 Report
Dear authors. I enjoyed reading your manuscript. The topic is very interesting. In my opinion, the article is suitable for the journal in its current form.
Author Response
Thank you
Round 2
Reviewer 1 Report
Authors addressed all the previous comments adequately
