Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR
Round 1
Reviewer 1 Report
This paper analyzes the characteristics of dynamic passenger demand by combining passenger’s past ticket purchasing data in each OD section during the pre-sale periods. I have a few reservations as follows;
Abstract
The authors need to add motivation to this research.
Line 18: Add the name of the city/country with the case study for a clear reader's understanding.
The authors need to add policy/practical implications.
1. Introduction
Line 33: Write the complete form of EMU the first time, then use it as an abbreviation.
Line 34: What plan?
Line 37 - 59: References are missing.
2. Literature Review
Line 97: Write the full form of HSR the first time, then use it as an abbreviation.
Line 146: What is the mean of OD? Please write in complete form here.
Add some studies from developed and developing economies as literature and then clearly mention the research gap(s).
3. Mathematical model
The authors have explained this section well.
4. Methods
Line 305: Why choose sparse method to simulate passenger ticket purchasing? Please mention the reason(s).
5. Case studies
Line 369: What is the reason for selecting this case study?
Figure 5.1: Explain it in the text for a clear understanding of the reader.
Line 416- 419: It needs to explain in the methodology section.
6. Conclusions
Policy and practical implications need to add.
Make some discussion to show the relevancy of this study with previous research, whether this research is consistent or non-consistent.
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
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Author Response.docx
Reviewer 2 Report
Very good paper
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Reviewer 3 Report
The manuscript is good, and it would be excellent if the authors would lay a better theoretical basis.
The problem that the authors consider was solved on a deterministic level back in the 70s of the last century using the method of integer linear programming. I mention it because of the authors and their further development. I do not require literary connection.
The authors do not recognize that they have done work in the field of hybrid artificial intelligence of Markov processes (ai method - uncertain reasoning, which is implicitly solved here by queuing systems) and swarm optimization (AI method, too). Similar works already exist in the hybrid of Markov processes and ant colony optimization. I do not require a literary connection, but I suggest that this fact be highlighted in the paper in a convenient way.
The relationship between Poisson processes and the exponential distribution is theoretically described by Conrad Palma's theorem. So, if the time between passenger requests is exponentially distributed, we arrive at the total number of requests that is distributed according to the Poisson distribution.
Furthermore, non-homogenous Poisson processes result from the non-stationary intensity of the exponential distribution between passenger requests (of course as a function of time). According to Raikov's theorem (from 1936), all Poisson processes (regardless of their intensity) ultimately result in a homogeneous Poisson process. For this reason, and above all because of raising the theoretical basis (practically a very high-quality completed manuscript), I suggest to the authors to point out in a suitable place that the non-homogeneous Poisson processes resulted from the non-stationary intensity of the exponential distribution of time between passenger requests, primarily.
A secondary reason for non-homogeneous Poisson processes arose from travel demands between different stations (which normally bring different intensities of integer demands that are distributed according to the Poisson distribution).
In the overall analytical apparatus, the authors are sovereign. The results are consistent with a large number of previous manuscripts I have read. Because of this, I deeply believe that the simulation of the results is also correct.
However, the description in the introduction to the simulation is modest. The verification of the set of input random numbers is not specified (testing, see and cite mauskript: Natural test for random numbers generator based on exponential distribution Tanackov, at all; Mathematics, 2019, 7(10), 920 because it has a deep theoretical connection with the processes that the authors are considering)
It is a pity that the parameter of verification of empirical and theoretical results was not highlighted! I expressly submit a request for the testing to be carried out, I suggest to the authors a non-parametric verification with the Signum test, with mandatory highlighting of the results in the form: p=0,XXXX. Fitting function, without parameters of the correlation cannot be considered!
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Reviewer 4 Report
Because the focus of your research is only on China, it is best if your title should be clear that the focus is only on China.
In the discussion section and conclusion please give some explanation that relates to the challenges that you have written in Line 48-54. Explain how the results of your research relate to the three challenges.
Did you make Figure 4.1 yourself or did you adopt it from another source? If you adopt it from another source, the reference must be written.
Add an explanation of your reasons why take the G19 (Beijing South - Shanghai Hongqiao) train running on the Beijing-Shanghai high-speed railway as an example to analyze. Why don't you choose another example?
Please add a discussion section. The discussion chapter should contain an in-depth interpretation of the results of your research. Compare with the results of previous studies. Give reasonable insight. Provide some future direction. The discussion section is the most important part of a scientific paper because it shows the depth and breadth of the researcher in understanding the research topic. At this point, it appears that your paper is very under-contributed because this section does not exist. You can divide the discussion into two parts, i.e a special discussion for the results of the case study, and a global discussion about your model. In the global discussion, your research really must be compared with other previous studies.
Add a limitation chapter explaining what your research lacks. For example, taking only a few case studies to represent a whole of China.
In the conclusion section, or you can create a new section, explain what your research contributions are from an academic point of view and from a practical point of view.
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Reviewer 5 Report
Dear Authors,
My review report of the study is attached. Please see it.
Best,
Comments for author File: 
Comments.pdf
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Reviewer 6 Report
1. Passenger purchasing demand is simulated based on sparse method. How to determine the constant value of parameter λij *? In Section 4.1 and 5.2, the method to determine λij * is not explained clearly. Is it sufficient to represent the purchasing demand when the value M is set as 100 in Section 5.2? Please argue the reason.
2. The definition of the particle position is not clearly given: how the scheme of ticket and allocation are modeled as particle position (e.g. the definition of the particle position as a vector)?
3. How to evaluate the fitness function? How to prove the convergence when the number of iterations reaches to maxgen?
4. In Chapter 5, the standby passenger ticket purchasing ratio θij is set as 0.9. Please explain why the ratio is set as a fixed value. Could this ratio also be used as a variable for optimization?
5. The pre-sale period is divided into 4 periods. Please explain the reason for the division. Could the division of the period be set as optimization variables?
6. There are many other heuristic optimization methods, including simulated annealing, tabu search, genetic methods. Could the author give more explanation why swarm intelligence is chosen?
7. The advantages of joint optimization of ticket allocation and dynamic pricing is only compared with the fixed value. Could the author also show the advantages to other methods, which has been listed in section 2.4?
Author Response
Thanks very much for taking your time to review this manuscript. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The authors incorporated the given comments.
Author Response
Thanks very much for taking your time to review this manuscript again. We really appreciate all your comments! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.Author Response File: Author Response.docx
Reviewer 4 Report
Thank you for the improvements you have made, but there are still some of my suggestions that you have not accommodated properly.
Regarding your response to suggestion number 3 from me, I think the citation should be written in the caption of the Figure
Your answer to my suggestion number 4 is still not clear. You still don't explain well why the G19 was chosen. Why not G21? Why not G15? Why not take 3 of them, ie. one for those who depart in the morning, one for those who depart at noon, and one for those who depart in the afternoon? So you have to explain well why it has to be G19, and why is it the only one. Your reasons must be based on scientific reasons accompanied by valid academic reference sources.
Your answer to my suggestion number 5, which is about the Discussion section, is still far from what I expected. When comparing the results of your research with previous studies, you should mention which studies you talking about. Then you tell us what the shortcomings of each of them were that your research was able to overcome. Discuss them one by one. When discussing those matters, you should cite the study, so that the reader can clearly know which study you are referring to. And do not just a story in the form of personal opinion, your discussion or comparisons has to be based on valid academic references
Author Response
Thanks very much for taking your time to review this manuscript again. We really appreciate all your comments and suggestions! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
Reviewer 6 Report
The authors responsed to all the comments and improved the manuscript with good quality. It is recommended to accept in its present form.
Author Response
Thanks very much for taking your time to review this manuscript again. We really appreciate all your comments! Please see the uploaded file for the response letter to the reviewer and the revised manuscript of the paper.
Author Response File: Author Response.docx
