A Study on Urban Traffic Congestion Pressure Based on CFD
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsHere are several areas that may raise concerns and limitations regarding the study on managing urban traffic congestion through the use of CFD (Computational Fluid Dynamics):
1. Viewing traffic as a fluid might miss important distinctions between how vehicles operate and the principles of fluid dynamics, including stop-and-go patterns, driver choices, and fluctuating speeds, potentially restricting the relevance of fluid mechanics to the study of traffic flow.
2. The analysis presumes straightforward parameter conversions between fluid and traffic dynamics, potentially oversimplifying intricate traffic patterns. A detailed analysis of how these assumptions influence the precision of the outcomes would enhance the overall value.
3. The study is missing empirical validation with real traffic data or field studies, which would aid in evaluating the accuracy and practicality of the CFD-based method in genuine urban environments.
4. The study employs 2D road fluid domains, which may overlook the intricacies and variations found in actual 3D traffic scenarios, including multi-level intersections, differing lane widths, and interactions with pedestrians.
5. Computational fluid dynamics models rely on the assumption of fluid smoothness and continuity, which may not be applicable in scenarios with heavy congestion characterised by unpredictable vehicle movements, lane changes, or abrupt stops that disturb flow patterns.
6. By broadly categorising congestion without a nuanced distinction, the study might miss particular elements inherent to each type, potentially affecting the effectiveness of mitigation strategies.
7. The behaviour of drivers, including their responses to congestion, how they navigate intersections, and their speed preferences, is not considered, which can significantly influence the development and continuation of congestion.
The analysis is exclusively conducted using Fluent software for computational fluid dynamics. Analysing other CFD tools or simulation software may reveal valuable information regarding the strength of the model and any limitations that could arise from the software itself.
External factors like weather conditions, time-of-day effects, road quality, and pedestrian movements, which impact traffic flow, are not addressed and could influence congestion solutions.
10. Although the study outlines optimisation measures, it fails to assess the economic viability of these design modifications, which is essential for practical implementation.
11. The analysis fails to consider the long-term effects of the proposed designs on congestion, which may influence the sustainability and longevity of the recommended solutions.
12. The study highlights the importance of geometric design modifications but overlooks additional strategies, such as optimising traffic signals, implementing policy changes, or promoting public transportation incentives, all of which are crucial for effective congestion management.
13. While the study suggests optimisation strategies, it does not provide quantitative benchmarks or metrics to assess improvements in traffic flow or reductions in accident rates.
14. The study fails to perform a sensitivity analysis on key parameters, including vehicle speed, flow rates, and intersection angles, to assess how changes in these factors affect congestion results.
15. The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
16. The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
17. The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
18. The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based methodology.
19. Lack of comparative evaluation with alternative methods to determine the advantages and drawbacks of computational fluid dynamics for optimising traffic flow.
20. Highlight the innovative approach of employing computational fluid dynamics in the analysis of traffic congestion and how it stands apart from conventional traffic modelling techniques. Emphasise the distinctive perspectives offered by computational fluid dynamics, including the intricate flow patterns and visualisation features that improve comprehension of congestion phenomena.
21. Include numerical data in both the summary and findings sections. Incorporate precise decreases in congestion indicators, such as average travel duration, enhancements in flow rates, or reductions in accident frequencies resulting from suggested design improvements.
22. Analyse the CFD method in relation to alternative traffic modelling techniques (e.g., macroscopic and microscopic traffic flow models). Examine how computational fluid dynamics offers benefits (or drawbacks) in understanding the intricacies of vehicular movement in contrast to traditional approaches.
23. Perform an analysis to explore how variations in parameters—like vehicle flow speed, intersection angle, and road design geometry—impact congestion results.
24. Examine and clearly articulate the influence of each variable in the model (e.g., GHI for climate studies, but in this context, elements such as vehicle density, speed, and intersection geometry).
25. Ensure that all technical terms are clearly defined, particularly those related to computational fluid dynamics and roadway systems that may not be universally understood.
26. Organise the abstract by distinctly outlining the study's aim, methodologies, results, and significance.
27. Discuss the significance of addressing urban traffic congestion, especially in cities experiencing rapid growth and development. Emphasise the reasons why CFD is especially effective in tackling these challenges and its potential for use in other urban settings facing comparable traffic problems.
28. Briefly outline any limitations of the study, including potential dependencies on assumptions in computational fluid dynamics modelling or the challenges of replicating actual traffic behaviours in simulations.
29. To enhance clarity and readability, I propose a list of symbols and notations to incorporate into the paper. This list should be included in a "Symbols and Notations" section either at the beginning or the end of the document for convenient access.
30. The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
31. The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based methodology.
32. The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Author Response
Response to Reviewer 1 Comments
Point 1: Viewing traffic as a fluid might miss important distinctions between how vehicles operate and the principles of fluid dynamics, including stop-and-go patterns, driver choices, and fluctuating speeds, potentially restricting the relevance of fluid mechanics to the study of traffic flow.
Response 1: Thank you for your valuable advice. Your insights into traffic flow research do provide us with a new perspective. We fully agree that there are indeed some limitations in treating traffic as fluid, especially when considering the specific mode of vehicle operation, driver 's decision-making behavior, and speed fluctuations. In the manuscript we also added some explanations, such as “It must be emphasized that in order to ensure the macroscopic flow characteristics of the fluid, we do not consider the driver's subjective factors such as stop-and-go mode, driver selection and fluctuation speed and the influence of external objective factors such as weather conditions”. These factors undoubtedly add additional dimensions to the complexity of traffic flow. However, we believe that comparing traffic flow with the principle of fluid dynamics is not to completely ignore these differences, but to try to understand and predict the behavior of traffic flow from a macro perspective. Fluid mechanics provides a powerful framework to help us capture some basic characteristics of traffic flow, such as continuity, momentum conservation, etc., which are also applicable in traffic flow[1]. In this way, we can predict and explain the macroscopic behavior of traffic flow to a certain extent, such as the formation and dissipation of congestion. We attach great importance to your feedback and will further explore how to better integrate these factors in subsequent studies to improve the applicability and accuracy of the model.
[1]Sun, D.; Lv, J.; Waller, S. T. In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method. Journal of Modern Transportation 2011, 19, 58-67. https://doi.org/10.1007/BF03325741
Point 2: The analysis presumes straightforward parameter conversions between fluid and traffic dynamics, potentially oversimplifying intricate traffic patterns. A detailed analysis of how these assumptions influence the precision of the outcomes would enhance the overall value.
Response 2: Thank you for your valuable advice. The assumption you mentioned about the direct parameter transformation between fluid and traffic dynamics is indeed an important point of discussion. By abandoning some redundant factors, we do affect the accuracy of the results, but the efficiency of our research is improved, and we solve the congestion problem from another perspective. We believe that by simplifying the complex traffic mode, although it will lead to reduced accuracy, it is more conducive to us to use fluid to study traffic flow, which makes the overall value improved.
For example, we use CFD software to analyze the traffic flow of roundabouts. The result of the analysis is that the congestion of roundabouts mostly occurs at the entrances and exits of intersections, and the pressure is greater. Although there are some limitations, such as local traffic fluctuations and emergencies. The impact is not considered, but our simplified model effectively captures the macro characteristics of traffic flow and overcomes these drawbacks to a certain extent. We are working on the integration of multidisciplinary knowledge in order to further enhance the overall value of research.
Therefore, we believe that this assumption is not a simple oversimplification, but captures the macroscopic characteristics of traffic flow to a certain extent. The core of the fluid dynamics model is to effectively describe the basic principles of continuity and momentum conservation in traffic flow. These principles also apply to traffic flow, especially in high-density situations where vehicle behavior tends to exhibit fluid-like characteristics. By treating traffic flow as a fluid, we can better understand and predict the overall behavior of traffic flow, such as the formation and dissipation of congestion.
In addition, we will also carefully consider the impact of the detailed analysis assumptions you mentioned on the accuracy of the results, and further explore this issue in subsequent studies. We believe that through continuous improvement and perfection, we can more fully reflect the complexity of traffic flow.
[1]X. Di, Y. Zhao, S. Huang and H. X. Liu, "A Similitude Theory for Modeling Traffic Flow Dynamics," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 900-911, March 2019, doi: 10.1109/TITS.2018.2837011.
Point 3: The study is missing empirical validation with real traffic data or field studies, which would aid in evaluating the accuracy and practicality of the CFD-based method in genuine urban environments.
Response 3: Thank you for your valuable advice. Our research aims to establish a basic theoretical framework to provide a starting point for subsequent empirical research. We believe that by verifying the basic assumptions and parameters of the model in the control environment first, we can lay a foundation for future field research.
In addition, our parameter settings and boundary conditions are not out of thin air, but based on a series of scenes observed in the city. Through investigation, it is found that congestion at roundabouts mostly occurs at the entrances and exits of intersections. This is because the entry vehicles and the exit vehicles have a conflict point, resulting in vehicle deceleration. When the traffic flow is large, it will cause congestion [1]. We have also made explanations in the manuscript, such as in the revised manuscript on page 9, line 318. “the pressure at the entrance and exit of the ring intersection is higher, this is because the vehicle entering the ring has a conflict point with the vehicle leaving the ring, resulting in the deceleration of the vehicle.”
We acknowledge that field research and real traffic data collection require a lot of resources and time. In the current research stage, we face certain resource and time constraints, which limits our ability to conduct large-scale field verification. We believe that scientific research is a gradual process. The purpose of this study is to provide a theoretical basis and preliminary verification for the CFD-based method. In future research, we plan to collect real data and conduct empirical verification through cooperation with urban traffic management departments. Although our research does not include field validation, we believe that we have contributed to this field by proposing new theoretical models and computational methods. We expect these methods to be empirically validated and further developed in future research.
[1] Wang, Z., Hou, S., Tang, B., et al. Study on traffic flow characteristics of irregular roundabout [J]. Journal of Dalian Jiaotong University, 2024, 45(02):17-23.DOI:10.13291/j.cnki.djdxac.2024.02.002..
Point 4: The study employs 2D road fluid domains, which may overlook the intricacies and variations found in actual 3D traffic scenarios, including multi-level intersections, differing lane widths, and interactions with pedestrians.
Response 4: Thank you for your valuable advice. This paper is based on the hypothesis of continuity, and this paper only analyzes the plane intersection model at present. For the road inequality, because the vehicle driver can subjectively control the speed, it can be approximately considered that there is no height difference. The multi-level intersection manuscript has not yet involved research in this area, but this proposal is very good, and we will conduct in-depth research in future work. For different lane widths, because the manuscript is based on the continuity assumption, the traffic flow is regarded as a whole, and the lane of the traffic flow is regarded as a pipe, so different lane widths will not affect the research. For the interaction with pedestrians, the manuscript assumes that the road studied is a closed road, and pedestrians can choose an overpass or an underground passage, so they will not be affected by pedestrians. If the interaction of pedestrians is considered, the research may not be carried out.
Point 5: Computational fluid dynamics models rely on the assumption of fluid smoothness and continuity, which may not be applicable in scenarios with heavy congestion characterised by unpredictable vehicle movements, lane changes, or abrupt stops that disturb flow patterns.
Response 5: Thank you for your valuable advice. The assumption of smoothness and continuity of the fluid is analyzed at the macro level. First of all, there is turbulence inside the fluid, and it is calculated by Fluent software, Re > 2000, which further proves that the interior of the fluid is changing at all times. We believe that this has included unpredictable vehicle movements. In addition, we also seriously considered the question you raised. For particularly severe congestion situations, the computational fluid dynamics model we have studied has not been solved. The focus of the manuscript is to consider alleviating congestion caused by the geometry of the road, but this proposal is very good. In the future, we will seriously consider and study this issue.
Point 6: By broadly categorising congestion without a nuanced distinction, the study might miss particular elements inherent to each type, potentially affecting the effectiveness of mitigation strategies.
Response 6: Thank you for your valuable advice. We fully understand the problem you point out, but the congestion characteristic is actually the slowing down of the flow rate. For the congestion caused by specific elements such as lane change, the internal movement of the fluid is naturally included. The manuscript focuses on the common problem of congestion, that is, the slowing down of the flow rate, and analyzes its differences through classification. In addition, we believe that a broad classification is the starting point for understanding complex phenomena. Through the extensive classification of congestion, we can construct a macro framework to identify and distinguish different types of congestion patterns. This macro perspective helps us to grasp the overall characteristics of traffic congestion and lay the foundation for further research.
Point 7: The behaviour of drivers, including their responses to congestion, how they navigate intersections, and their speed preferences, is not considered, which can significantly influence the development and continuation of congestion.
Response 7: Thank you for your valuable advice. We read and thought deeply about this question. The problems you point out are very critical, and the driver 's behavior does have a non-negligible impact on the development and sustainability of traffic congestion. In the manuscript, we also added some notes that “it must be emphasized that in order to ensure the macroscopic flow characteristics of the fluid, we do not consider the influencing factors such as stop-and-go mode, driver selection and fluctuation speed”. We understand your concerns that drivers ' responses to congestion, their behavior at intersections, and speed preferences are not fully considered in the study, which may have an impact on the effectiveness of congestion mitigation strategies, but due to the presence of internal turbulence in the fluid, we believe that most of the driver 's random choices are included. And in practical research, due to the complexity of data collection and analysis, it is challenging to fully consider these factors. Our goal is to provide a broader perspective to analyze and mitigate traffic congestion, but that doesn 't mean we ignore the importance of driver behavior. In fact, we believe that macro-level congestion mitigation measures can indirectly affect drivers ' behavior by providing them with a better traffic environment.We believe that future research can further explore the specific impact of driver behavior on traffic congestion and incorporate it into traffic congestion mitigation strategies. We hope that in the follow-up study, we can combine more micro data to deeply analyze the impact of driver behavior on traffic flow, and formulate more accurate mitigation measures accordingly.
Point 8: The analysis is exclusively conducted using Fluent software for computational fluid dynamics. Analysing other CFD tools or simulation software may reveal valuable information regarding the strength of the model and any limitations that could arise from the software itself.
Response 8: Thank you for your valuable advice. Your views on using a variety of CFD tools to verify the strength of the model and determine software limitations are very insightful. But we consider that all CFD software is based on the same fluid mechanics model. The same parameters and boundary conditions are input into different software and the results are the same, so we decided to focus on Fluent software to ensure the depth and quality of the study.
Point 9: External factors like weather conditions, time-of-day effects, road quality, and pedestrian movements, which impact traffic flow, are not addressed and could influence congestion solutions.
Response 9: Thank you for your valuable advice. We read and thought deeply about this question. External factors have a non-negligible impact on the development and persistence of traffic congestion. We understand your concerns, and in the manuscript we have added some notes “It must be emphasized that in order to ensure the macroscopic flow characteristics of the fluid, we do not consider the driver's subjective factors such as stop-and-go mode, driver selection and fluctuation speed and the influence of external objective factors such as weather conditions.“.
These factors undoubtedly add additional dimensions to the complexity of traffic flow. However, we believe that the focus of the article is to find out the common characteristics of congestion and solve it. We attach great importance to your feedback and will further explore how to better integrate these factors in subsequent research and incorporate them into traffic congestion solutions..
Point 10: Although the study outlines optimisation measures, it fails to assess the economic viability of these design modifications, which is essential for practical implementation.
Response 10: Thank you for your valuable advice. The problem you point out is very pertinent, but what we consider is that we only analyze the technical feasibility at present. We hope to first recognize its technical feasibility, and then analyze its economic feasibility. But this proposal is very good, we will seriously consider and study this in the future.
Point 11: The analysis fails to consider the long-term effects of the proposed designs on congestion, which may influence the sustainability and longevity of the recommended solutions.
Response 11: Thank you for your valuable advice. Your problem is that our analysis does not fully consider the long-term impact of the proposed design on congestion, which is indeed an important consideration. We understand your concerns and would like to provide some clarification and explanation.
In the past, as long as the road did not occur too much damage, we believe that the short-term impact is at least 5 years or more, you may consider the long-term impact is more than 30 years, policy and road changes and other circumstances caused by the impact. Because long-term data are often difficult to obtain and may be affected by various unpredictable factors, this may affect the accuracy and reliability of the analysis results. And in view of the urgency of the current traffic congestion problem, we believe that it is more important to give priority to solutions that can be implemented and evaluated in the short term to respond quickly and alleviate the current traffic pressure. In addition, we believe that the long-term impact and short-term impact of the law is consistent, we come to the results are also applicable to long-term planning, and for the short-term impact of the effect will be better. Nevertheless, we fully agree on the importance of long-term effects, and that transportation solutions should be a dynamic, continuous improvement process. Therefore, we plan to collect feedback based on actual results after implementing short-term solutions, adjust long-term planning and leave space for further exploration of our research in future research. We plan to incorporate more long-term data and models in our follow-up work to evaluate and optimize our solutions.
Point 12: The study highlights the importance of geometric design modifications but overlooks additional strategies, such as optimising traffic signals, implementing policy changes, or promoting public transportation incentives, all of which are crucial for effective congestion management.
Response 12: Thank you for your valuable advice. We understand your concern about the importance of considering multiple strategies in research to achieve effective congestion management. We agree that optimizing traffic signals, implementing policy changes and promoting public transport incentives are all important components of traffic management. As you pointed out, traffic management is a multifaceted and complex issue involving a variety of strategies. Our research focuses on providing a new perspective for solving the problem of road congestion and broadening the thinking of engineering practice in this field, because we believe that in-depth exploration of specific fields can provide more in-depth insights and technological progress. We also recognize that traffic management is a systematic project, which needs to consider a variety of factors. Our research is part of this system project and we hope that future research will integrate more strategies, including traffic signal optimization, policy changes and public transport incentives. We believe that through gradual progress, we can solve the problem of traffic congestion more comprehensively.
Point 13: While the study suggests optimisation strategies, it does not provide quantitative benchmarks or metrics to assess improvements in traffic flow or reductions in accident rates.
Response 13: Thank you for your valuable advice. It is a good idea to provide quantitative benchmarks or indicators to assess improvements in traffic flow or reductions in accident rates. We recognize the importance of quantitative assessment in the study of traffic flow management and optimization strategies. Therefore, we use the change of pressure index to evaluate. The pressure can reflect the congestion degree of traffic flow in the roundabout, including the indirect evaluation of traffic flow and accident rate. We also made an explanation in the manuscript, such as ”When using Fluent software to calculate the established model, due to the local stress concentration caused by the geometric shape of the model, there is a certain error in the local data, especially the influence on the speed is too large, so in the later model simulation, the pressure is used as the index of traffic congestion degree”.
Point 14: The study fails to perform a sensitivity analysis on key parameters, including vehicle speed, flow rates, and intersection angles, to assess how changes in these factors affect congestion results.
Response 14: Thank you for your valuable advice. We are sorry that due to our written negligence, the impact of parameter changes on congestion is not explained. We modified the manuscript and added a discussion on how changes in parameters such as vehicle speed and intersection angles affect congestion results. ”By changing the vehicle entrance angle of the intersection, in order to reduce the conflict point of the vehicle at the intersection, thereby reducing the incidence of traffic accidents and promoting the smooth flow of vehicles. In addition, due to the intersection of roads, the confluence point is generated to slow down the vehicle and increase the pressure. By changing the intersection angle of the road intersection, the traffic pressure can also be alleviated.
The parallel ramp entrance effectively reduces the traffic conflict and congestion caused by the direct merging of vehicles on the main road by extending the parallel distance before the vehicles merge into the main road, thus reducing the pressure on the main road, making the traffic flow more stable and improving the overall traffic efficiency.
For speed, too fast speed may lead to safety problems and increase the risk of accidents, thus causing or exacerbating congestion. If the speed is too slow, it may lead to a decrease in the efficiency of traffic flow, increase the queue length of vehicles, and form congestion. In the case of non-recurring congestion, such as temporary narrow roads caused by accidents or emergencies, the pressure at the accident point can be reduced by reducing the speed of the vehicle, but it may also lead to an increase in the length of the rear vehicle queue. Therefore, it is necessary to find a balance point that can not only ensure traffic safety, but also maintain the efficiency of traffic flow.”
Point 15: The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Response 15: Thank you for your valuable advice. The problem you pointed out is very pertinent, and human factors do have a non-negligible impact on the development and persistence of traffic congestion. We understand your concern that the failure to fully consider the impact of human impact on congestion in the study may have an impact on congestion solutions. We believe that although individual driver behavior has an impact on traffic flow, these effects are often averaged at the macro level. In high-density traffic flow, the influence of individual behavior tends to be smooth, while the macroscopic characteristics such as continuity and momentum conservation of the overall traffic flow become more significant. Therefore, the CFD-based method is reasonable in simulating these macroscopic characteristics.
Point 16: The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Response 16: Thank you for your valuable advice. This problem has been explained in Response 15, and here we explain it more deeply for you. At this stage, we aim to accurately capture the physical characteristics of traffic flow through CFD methods, which provides us with a controllable starting point. We believe that only after fully understanding the basic physical processes can we more effectively simulate and analyze the impact of human factors and driver behavior. Integrating human factors and driver behavior will significantly increase the complexity of the model, which not only puts forward higher requirements for computing resources, but also may affect the stability and interpretability of the model. We believe that prematurely incorporating these complex factors into the model may introduce unnecessary uncertainty without fully understanding how these complex factors affect traffic flow.
Point 17: The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Response 17: Thank you for your valuable advice. This problem has been explained in Response 15, and here we explain it more deeply for you. We would like to point out that although these factors have a significant impact on traffic flow in theory, in practical research, due to the complexity of data collection and analysis, it is challenging to fully consider these factors. We believe that future research can further discuss the specific impact of these human factors on traffic congestion and incorporate them into congestion solutions.
Point 18: The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based methodology.
Response 18: Thank you for your valuable advice. We explained this question in Response 15, and we 'll explain to you in more detail here that individual driver behavior can have a significant impact on traffic flow, for example in low-density traffic flows or specific traffic events. In these cases, we really need to consider the impact of driver behavior. However, our research focuses on the simulation and prediction of macroscopic traffic flow, rather than the detailed simulation of individual traffic events. We would like to emphasize that any model has its limitations, including CFD-based traffic flow models. Our goal is to provide a macroscopic model that captures the main characteristics of traffic flow, rather than a model that can simulate all the details. We believe that through appropriate model selection and application, we can provide valuable traffic flow prediction and analysis while maintaining the practicality and operability of the model.
Point 19: Lack of comparative evaluation with alternative methods to determine the advantages and drawbacks of computational fluid dynamics for optimising traffic flow.
Response 19: Thank you for your valuable advice. Sun et al.[1] used computational fluid dynamics ( CFD ) modeling method to analyze traffic congestion in depth, and drew conclusions.“As a numerical solution, the CFD method has three advantages compared with general traffic simulation: (1) it can reduce the computational workload; (2) it can provide more detailed description regarding traffic flow waves; (3) it can estimate traffic conditions of the upstream as well as the past. However, the accuracy of the CFD method is bound to two limited assumptions. First, there exists a speed-density-flow relationship; second, the studied road segment has minimum external interruptions. The applicability and capabilities of the method were demonstrated through theoretical analysis as well as field data collected in San Antonio, Texas. The theoretical and practical applications discussed in this paper revealed the great potential of the CFD approach for transportation study.”Liu et al.[2] used SUMO software to solve the congestion problem, and the conclusion is similar.
[1]Sun, D.; Lv, J.; Waller, S. T. In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method. Journal of Modern Transportation 2011, 19, 58-67. https://doi.org/10.1007/BF03325741
[2]Liu, Z.; Wu, Y.; Cao, S.; Zhu, L.; Shen, G. A ramp metering method based on congestion status in the urban freeway. IEEE Access 2020, 8, 76823-76831. https://doi.org/10.1109/ACCESS.2020.2990646
Point 20: Highlight the innovative approach of employing computational fluid dynamics in the analysis of traffic congestion and how it stands apart from conventional traffic modelling techniques. Emphasise the distinctive perspectives offered by computational fluid dynamics, including the intricate flow patterns and visualisation features that improve comprehension of congestion phenomena.
Response 1: Thank you for your valuable advice. I am sorry that due to our written supervision, the description of innovative methods and unique research perspectives is not clear enough. We have modified the manuscript and added the advantages of computational fluid dynamics research compared with traditional methods. We understand your concerns. As a result, we have added more text descriptions to modify the manuscript, such as “Traffic flow is regarded as a continuous medium, and the overall characteristics of traffic flow, such as flow, density and speed, are concerned. The advantage of these models is that they can intuitively grasp the overall characteristics of traffic flow, and it is easy to obtain analytical solutions in the case of simplification. Compared with conventional traffic software, CFD software has many advantages in studying traffic problems, such as high cost-effectiveness, flexibility, high time efficiency, powerful simulation function, ability to simulate complex flow conditions and provide comprehensive and detailed information. One of the unique perspectives provided by the CFD method is its powerful visualization features. CFD software can display graphics such as fluid dynamics, pressure distribution and temperature changes in great detail, which provides intuitive help for understanding the complexity of traffic flow. Compared with traditional methods, the visualization effect of CFD can help researchers and decision makers understand the dynamic changes and potential causes of traffic congestion more intuitively”. We hope to address your concerns with further explanation. Thank you again for your valuable comments.
Point 21: Include numerical data in both the summary and findings sections. Incorporate precise decreases in congestion indicators, such as average travel duration, enhancements in flow rates, or reductions in accident frequencies resulting from suggested design improvements.
Response 21: Thank you for your valuable advice. We recognize the importance of data illustration in the study of traffic flow management and optimization strategies. The optimization strategies we study are based on theoretical models that have been designed to take into account the potential impact of traffic flow and accident rates. However, the research is to equivalent the traffic flow to a fluid, and can not clearly give the specific traffic flow or the change of the accident rate. Although we do not directly provide the value of the reduction in the frequency of accidents caused by design improvements, our conclusions refer to the effect evaluation after the implementation of the model prediction and optimization strategy, that is, the change of the pressure index, which includes the indirect evaluation of traffic flow and accident rate.
Point 22: Analyse the CFD method in relation to alternative traffic modelling techniques (e.g., macroscopic and microscopic traffic flow models). Examine how computational fluid dynamics offers benefits (or drawbacks) in understanding the intricacies of vehicular movement in contrast to traditional approaches.
Response 22: Thank you for your valuable advice. We are sorry that due to our written oversight, the difference between the CFD method and other traffic modeling methods is not explained. We modified the manuscript and added the advantages of computational fluid dynamics research compared with traditional methods, such as “Compared with conventional traffic software, CFD software has many advantages in studying traffic problems, such as high cost-effectiveness, flexibility, high time efficiency, powerful simulation function, ability to simulate complex flow conditions and provide comprehensive and detailed information. One of the unique perspectives provided by the CFD method is its powerful visualization features. CFD software can display graphics such as fluid dynamics, pressure distribution and temperature changes in great detail, which provides intuitive help for understanding the complexity of traffic flow. Compared with traditional methods, the visualization effect of CFD can help researchers and decision makers understand the dynamic changes and potential causes of traffic congestion more intuitively”. We hope to address your concerns with further explanation. Thank you again for your valuable comments.
Point 23: Perform an analysis to explore how variations in parameters—like vehicle flow speed, intersection angle, and road design geometry—impact congestion results.
Response 23: Thank you for your valuable advice. We are very sorry that due to our written negligence, the impact of each variable is not fully explained. Thank you for your valuable advice. We are sorry that due to our written negligence, the impact of parameter changes on congestion is not explained. We modified the manuscript and added a discussion on how changes in parameters such as vehicle speed, intersection angle, and road design geometry affect congestion results, such as ”By changing the vehicle entrance angle of the intersection, in order to reduce the conflict point of the vehicle at the intersection, thereby reducing the incidence of traffic accidents and promoting the smooth flow of vehicles. In addition, due to the intersection of roads, the confluence point is generated to slow down the vehicle and increase the pressure. By changing the intersection angle of the road intersection, the traffic pressure can also be alleviated.
The parallel ramp entrance effectively reduces the traffic conflict and congestion caused by the direct merging of vehicles on the main road by extending the parallel distance before the vehicles merge into the main road, thus reducing the pressure on the main road, making the traffic flow more stable and improving the overall traffic efficiency.
For speed, too fast speed may lead to safety problems and increase the risk of accidents, thus causing or exacerbating congestion. If the speed is too slow, it may lead to a decrease in the efficiency of traffic flow, increase the queue length of vehicles, and form congestion. In the case of non-recurring congestion, such as temporary narrow roads caused by accidents or emergencies, the pressure at the accident point can be reduced by reducing the speed of the vehicle, but it may also lead to an increase in the length of the rear vehicle queue. Therefore, it is necessary to find a balance point that can not only ensure traffic safety, but also maintain the efficiency of traffic flow.”
Point 24: Examine and clearly articulate the influence of each variable in the model (e.g., GHI for climate studies, but in this context, elements such as vehicle density, speed, and intersection geometry).
Response 24: Thank you for your valuable advice. We are very sorry that due to our written negligence, the impact of each variable is not fully explained. We systematically review the full text to ensure that the effects of each variable are clearly state, such as “By changing the vehicle entrance angle of the intersection, in order to reduce the conflict point of the vehicle at the intersection, thereby reducing the incidence of traffic accidents and promoting the smooth flow of vehicles. In addition, due to the intersection of roads, the confluence point is generated to slow down the vehicle and increase the pressure. By changing the intersection angle of the road intersection, the traffic pressure can also be alleviated.
The parallel ramp entrance effectively reduces the traffic conflict and congestion caused by the direct merging of vehicles on the main road by extending the parallel distance before the vehicles merge into the main road, thus reducing the pressure on the main road, making the traffic flow more stable and improving the overall traffic efficiency.
For speed, too fast speed may lead to safety problems and increase the risk of accidents, thus causing or exacerbating congestion. If the speed is too slow, it may lead to a decrease in the efficiency of traffic flow, increase the queue length of vehicles, and form congestion. In the case of non-recurring congestion, such as temporary narrow roads caused by accidents or emergencies, the pressure at the accident point can be reduced by reducing the speed of the vehicle, but it may also lead to an increase in the length of the rear vehicle queue. Therefore, it is necessary to find a balance point that can not only ensure traffic safety, but also maintain the efficiency of traffic flow.”
Point 25: Ensure that all technical terms are clearly defined, particularly those related to computational fluid dynamics and roadway systems that may not be universally understood.
Response 25: Thank you for your valuable advice. We are sorry that due to our written negligence, the definition of terminologies is not fully explained. We systematically check the full text to ensure that each term has its definition.
Point 26: Organise the abstract by distinctly outlining the study's aim, methodologies, results, and significance.
Response 26: Thank you for your valuable advice. Thank you for your valuable advice. Due to our negligence in writing, the abstract part of the manuscript does not clearly present the information to be expressed in this article. In response to this situation, we have modified the manuscript to use clear writing logic to describe the content and focus of the article.
Abstract: With the rapid advancement of urbanization, the problem of traffic congestion in cities has become increasingly severe. Effectively managing traffic congestion is crucial for sustainable urban development. Previous studies have indicated that fluid dynamics theory can be applied to address flow problems in transportation, and this article aims at utilize CFD to solve congestion issues in urban road traffic. Firstly, a similarity analysis is conducted between fluids and traffic flow at the theoretical level. By converting parameters, the formula of fluid is derived into the formula of traffic flow, thus demonstrating the feasibility of using CFD in traffic flow research. On this basis, targeting recurrent congestion and non-recurrent congestion scenarios, 2D road fluid domains and constraints are constructed based on the common characteristics of each congestion type area. By using Fluent software to analyze the flow conditions under different congestion characteristics, use the smoothness of fluid motion to find out the problems causing traffic congestion and conduct analysis to reveal the microscopic mechanism behind congestion formation. For different types of congestion, in order to clarify the effectiveness of congestion mitigation measures, the geometric design of road intersections and diversion measures are discussed in depth. The traffic pressure is analyzed by adjusting the vehicle inlet angle at intersections or controlling the vehicle flow speed. Finally, the optimal design scheme is obtained by comparative analysis. It is concluded that for the roundabout, it is optimal to change the entrance angle to 20°. For the on-ramp merging area, it is optimal to set the ramp entrance as a parallel ramp. For recurrent congestion, it is required to pass at an optimal speed of 30 km/h. Based on the theory of previous studies, this paper further proves that the congestion degree of traffic flow under specific assumptions can be expressed by the pressure of fluid. It also provides new ideas for optimizing urban road design and solving vehicle traffic congestion problems.
Point 27: Discuss the significance of addressing urban traffic congestion, especially in cities experiencing rapid growth and development. Emphasise the reasons why CFD is especially effective in tackling these challenges and its potential for use in other urban settings facing comparable traffic problems.
Response 27: Thank you for your valuable advice. Due to our negligence in writing, we are very sorry for the lack of discussion on the importance of CFD in solving traffic congestion. In order to make readers better read the article, we have been modified in the manuscript,such as “CFD software has many advantages in studying traffic problems, such as high cost-effectiveness, flexibility, high time efficiency, powerful simulation function, ability to simulate complex flow conditions and provide comprehensive and detailed information. One of the unique perspectives provided by the CFD method is its powerful visualization features. CFD software can display graphics such as fluid dynamics, pressure distribution and temperature changes in great detail, which provides intuitive help for understanding the complexity of traffic flow. Compared with traditional methods, the visualization effect of CFD can help researchers and decision makers understand the dynamic changes and potential causes of traffic congestion more intuitively.”
Sun et al.[1] used computational fluid dynamics ( CFD ) modeling method to analyze traffic congestion in depth, and the conclusion is similar.
[1]Sun, D.; Lv, J.; Waller, S. T. In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method. Journal of Modern Transportation 2011, 19, 58-67. https://doi.org/10.1007/BF03325741
Point 28: Briefly outline any limitations of the study, including potential dependencies on assumptions in computational fluid dynamics modelling or the challenges of replicating actual traffic behaviours in simulations.
Response 28: Thank you for your valuable advice. Due to our negligence in writing, we feel very sorry for the lack of the limitations of the proposed approach. In order to make readers better read the article, we have been modified in the manuscript. Such as “The limitation of this paper is based on a specific continuity assumption, and discrete traffic flow cannot be studied at present. In addition, the accuracy of the CFD method is limited by two limited assumptions. First of all, there must be a speed-density-flow relationship ; secondly, the road section studied has the smallest external interference. Therefore, the goal of future work is to analyze the applicability of more types of working conditions and try to study discrete traffic flow. However, these do not affect the research of the current article.”
Point 29: To enhance clarity and readability, I propose a list of symbols and notations to incorporate into the paper. This list should be included in a "Symbols and Notations" section either at the beginning or the end of the document for convenient access.
Response 3: Thank you for your valuable advice. Due to our negligence in writing, we can not meet the requirements of readers. We have revised the manuscript and merged the symbols into the paper. This list is placed at the beginning of the document for easy access, hoping to give readers a good reading experience.
Point 30: The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Response 30: Thank you for your valuable advice. The problem you pointed out is very pertinent. Human factors do have a non-negligible impact on the development and persistence of traffic congestion. We understand your concern that the failure to fully consider the impact of human impact on congestion in the study may have an impact on congestion solutions. However, we would like to point out that although these factors have a significant impact on traffic flow in theory, in practical research, due to the complexity of data collection and analysis, it is challenging to fully consider these factors. And we think that the turbulence inside the fluid also involves the autonomy of the driver. We believe that future research can further discuss the specific impact of these human factors on traffic congestion and incorporate them into congestion solutions.
Point 31: The model fails to consider human factors or driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based methodology.
Response 4: Thank you for your valuable advice. This problem has been explained in Response 30, and here we explain it more deeply for you. We believe that although individual driver behavior has an impact on traffic flow, these effects are often averaged at the macro level. In high-density traffic flow, the influence of individual behavior tends to be smooth, while the macroscopic characteristics such as continuity and momentum conservation of the overall traffic flow become more significant. Therefore, the CFD-based method is reasonable in simulating these macroscopic characteristics..
Point 32: The model overlooks human factors and driver behaviour, which can greatly impact traffic flow and congestion, yet are challenging to integrate into a CFD-based approach.
Response 4: Thank you for your valuable advice. We 've explained this question in Response 30, and we 'll go a step further to explain to you that individual driver behavior can have a significant impact on traffic flow, for example in low-density traffic flows or specific traffic events. In these cases, we really need to consider the impact of driver behavior. However, our research focuses on the simulation and prediction of macroscopic traffic flow, rather than the detailed simulation of individual traffic events. We would like to emphasize that any model has its limitations, including CFD-based traffic flow models. Our goal is to provide a macroscopic model that captures the main characteristics of traffic flow, rather than a model that can simulate all the details. We believe that through appropriate model selection and application, we can provide valuable traffic flow prediction and analysis while maintaining the practicality and operability of the model.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper studies traffice congestion by using tools from fluid dynamics.
The study addresses an interesting topic and hence, it is definitely worth being published provided the following issues have been tackled.
1) The key congestion figure seems to be the pressure (cf. Figure 8). However, it is unclear whether the pressure is static or dynamic. (Probably it is the latter; but the reader should not be forced to guess.)
2) Table 1 lists the system variables including pressure. Is this the dynamic pressure? Equations for both, static and dyanamic pressure should be provided.
3) The introduction does a good job. However, there should be added more different works:
A powerful method is Susceptible-Infected-Recovered (SIR) model (DOI: 10.1109/ACCESS.2024.3370474 IEEE Access)
Moreover, the integration of internet of things (IoT) in managing urban congestion is crucial, especially for dynamic data collection, vehicle flow prediction, and implementing control strategies like speed regulation and signal adjustments. Data congestion is strongly related to vehicle congestion. For example, DOI 10.1109/JIOT.2022.3142324(IEEE Internet of Things), the authors followed a stochatic approach using data-flow graphs to model data congestion. Stochastic data-flow graphs for congestion modelling should be added to the list of literature.
Finally, machine learning techniques in general, and neural networks in particular, play an important role in congestion forecasting. An overview of machine learning techniques for suggestion modeling should be added, as well.
4) What are the limitations of the proposed approach?
Author Response
Response to Reviewer 2 Comments
Point 1: The key congestion figure seems to be the pressure (cf. Figure 8). However, it is unclear whether the pressure is static or dynamic. (Probably it is the latter; but the reader should not be forced to guess.)
Response 1: Thank you for your valuable advice. Due to our negligence in writing, we are very sorry for the lack of a detailed description of the stress of the manuscript Figure 8. We have modified it in the manuscript to avoid unclear expression, such as ”In the context of traffic flow, static pressure can be compared to the pressure distribution of traffic flow on the road, that is, the pressure change caused by vehicle movement is not considered, but the pressure caused by vehicle existence on the road is concerned. The purpose of the article is to alleviate traffic flow by changing the geometric design of the road, so the pressure in the figure is static pressure.”
Point 2: Table 1 lists the system variables including pressure. Is this the dynamic pressure? Equations for both, static and dynamic pressure should be provided.
Response 2: Thank you for your valuable advice. The pressure in Table 1 is the dynamic pressure. Due to our negligence in writing, the pressure of the manuscript Table 1 does not clearly present the information to be expressed in this article. In addition, we are very sorry about the lack of static pressure and dynamic pressure equations. We have revised the manuscript and added equations, hoping to benefit the reader 's reading experience.
Point 3: The introduction does a good job. However, there should be added more different works:
A powerful method is Susceptible-Infected-Recovered (SIR) model (DOI: 10.1109/ACCESS.2024.3370474 IEEE Access)
Moreover, the integration of internet of things (IoT) in managing urban congestion is crucial, especially for dynamic data collection, vehicle flow prediction, and implementing control strategies like speed regulation and signal adjustments. Data congestion is strongly related to vehicle congestion. For example, DOI 10.1109/JIOT.2022.3142324(IEEE Internet of Things), the authors followed a stochatic approach using data-flow graphs to model data congestion. Stochastic data-flow graphs for congestion modelling should be added to the list of literature.
Finally, machine learning techniques in general, and neural networks in particular, play an important role in congestion forecasting. An overview of machine learning techniques for suggestion modeling should be added, as well.
Response 3: Thank you for your valuable advice. Due to our negligence in writing, we are very sorry for the number of references cited. Since there are only a few studies in this research, we can only quote a small amount of literature. We are very grateful that you can recommend such excellent works for our reference. We found that the articles you recommended (DOI : 10.1109 / ACCESS.2024.3370474 IEEE Access) and DOI 10.1109 / JIOT.2022.3142324 (IEEE Internet of Things) are very helpful to our papers, and we think they must appear in our papers. It is hoped that readers will have a better reading experience..
Point 4: What are the limitations of the proposed approach?
Response 4: Thank you for your valuable advice. Due to our negligence in writing, we feel very sorry for the lack of the limitations of the proposed approach. In order to allow readers to better read the article, we have modified the manuscript and added the limitations of the method in many places, such as ”The limitation of this paper is based on a specific continuity assumption, and discrete traffic flow cannot be studied at present. In addition, the accuracy of the CFD method is limited by two limited assumptions. First of all, there must be a speed-density-flow relationship ; secondly, the road section studied has the smallest external interference. Therefore, the goal of future work is to analyze the applicability of more types of working conditions and try to study discrete traffic flow. However, these do not affect the research of the current article.”
Author Response File: Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsIn this paper, the authors verify the fluid dynamics characteristics of traffic flow through the continuum medium hypothesis and then adopt CFD theory to model and simulate urban traffic flow.
Before recommendation for publication, I would suggest a few points as follows:
The Abstract lacks mention of the most important points that distinguish the results of this paper from the previous one.
In line 130 add a sub-sub-sub section for Deformability.
The resolution for Figure 1 is very bad use another extension of the figure or compile again.
Use a full stop or comma after each equation. e.g. use comma after equation 1 and full stop after equation 2. Check the rest of the equations accordingly.
Check again equation 4. There is a mistake in writing the equation.
Line no. 309 is about figure 5, I didn't find any figure 5 in the manuscript. there is a typo in the caption of the figure.
Line 315 is Table 3, there is a mistake. The authors should proofread once before submitting the manuscript.
Rewrite the conclusive part of the manuscript and add some future work. What is the impact of this study on the readers? Explain your future work
Comments on the Quality of English Language
There are some typographic errors in the whole manuscript. Check it. Unfortunately, the language and sentence structures of this manuscript are sometimes incomprehensible. The paper contains many stale sentences and inappropriate verbs, so it needs thorough rewriting, editing, and language revision to allow for a proper peer review.
Author Response
Response to Reviewer 3 Comments
Point 1: The Abstract lacks mention of the most important points that distinguish the results of this paper from the previous one.
Response 1: Thank you for your valuable comments. Due to our negligence in writing, we feel very sorry for the lack of distinction from previous results. We have modified it in the manuscript, hoping to make readers better understand the idea of the paper.
Based on the theory of previous studies, this paper further proves that the congestion degree of traffic flow under specific assumptions can be expressed by the pressure of fluid. It also provides new ideas for optimizing urban road design and solving vehicle traffic congestion problems.
Point 2: In line 130 add a sub-sub-sub section for Deformability.
Response 2: Thank you for your valuable advice. We have subdivided the Deformability section.
Point 3: The resolution for Figure 1 is very bad use another extension of the figure or compile again.
Response 3: Thank you for your valuable advice. Due to our negligence in writing, the use of Figure 1 can not meet the requirements of readers. We have recompiled this picture, hoping to benefit the reader 's reading experience.
Point 4: Use a full stop or comma after each equation. e.g. use comma after equation 1 and full stop after equation 2. Check the rest of the equations accordingly.
Response 4: Thank you for your valuable comments. Due to our negligence in writing, we feel very sorry for the lack of commas or full stops. We modified it in the manuscript to avoid the recurrence of such problems.
Point 5: Check again equation 4. There is a mistake in writing the equation.
Response 5: Thank you for your valuable comments. Due to our negligence in writing, we are very sorry for the writing errors of Equation 4. We have modified it in the manuscript to avoid unclear expression.
Point 6: Line no. 309 is about figure 5, I didn't find any figure 5 in the manuscript. there is a typo in the caption of the figure.
Response 6: Thank you for your valuable comments. Due to our negligence in writing, we are very sorry for the title errors in Figure 5. We modified it in the manuscript to avoid the recurrence of such problems.
Point 7: Line 315 is Table 3, there is a mistake. The authors should proofread once before submitting the manuscript.
Response 7: Thank you for your valuable comments. Due to our negligence in writing, we are very sorry for the title errors in Table 3. We modified it in the manuscript to avoid the recurrence of such problems.
Point 8: Rewrite the conclusive part of the manuscript and add some future work. What is the impact of this study on the readers? Explain your future work.
Response 8: Thank you for your valuable advice. Due to our negligence in writing, the writing of the conclusion is not perfect. In view of this situation, we have revised the conclusion part of the manuscript and added the prospect of future work and the impact on readers, hoping to benefit the reader 's reading experience. Such as “Based on the similarity between fluid dynamics and urban traffic flow, this paper verifies the fluid dynamics characteristics of traffic flow through the continuum medium hypothesis and then adopts CFD theory to model and simulate urban traffic flow. The main conclusions are as follows :
(1)CFD applies to the study of traffic flow. On the theoretical level, through the similarity analysis of traffic flow and fluid and the transformation of eigenvalue parameters, the theoretical basis of CFD applied to traffic flow is established. The output results in the simulation are the same as the actual traffic flow, which verifies the applicability of CFD theory in traffic flow research.
(2) For recurrent congestion, reducing the intersection angle of the road can effectively improve the traffic pressure. For the roundabout, when conditions permit, changing the inlet angle from 45 degrees to 15 degrees reduces the pressure at the inlet by 19.2%, and the maximum pressure value in the ring can be reduced by 16.4%; For the on-ramp merging area, when conditions permit, the ramp is changed from a direct ramp with an entrance angle of 60° to a parallel ramp. The traffic pressure on the main road is reduced by 13.5%, the ramp is reduced by 26.5%, and the maximum pressure value in the road is reduced by 26.7%. The setting of the entrance angle of the intersection has a great influence on the traffic flow pressure. To obtain higher traffic capacity, it should be considered in the design and transformation of the intersection.
(3) For non-recurrent congestion, deceleration guidance upstream is conducive to the smooth passage of vehicles through the accident section. In the range of 20km/h to 60km/h, the accident section shows a significant change in pressure. The smaller the speed of traffic control, the lower the pressure of the accident section. In reality, a long queue may be formed, but it will reduce the probability of accidents. The vehicle speed should be properly controlled according to the actual traffic flow to obtain higher capacity.
The limitation of this paper is based on a specific continuity assumption, and discrete traffic flow cannot be studied for the time being. Therefore, the future work goal is to analyze the applicability of more types of working conditions and try to study discrete traffic flow.
In summary, the content discussed in this paper reveals the great potential of CFD in traffic research. CFD theory provides a new way to solve road traffic problems, and through the similarity analysis of traffic flow and fluid, make up for the deficiency of the existing theory. This study provides valuable information on the impact of different road conditions on road traffic flow pressure, thereby helping traffic engineers make better urban road design decisions. It also provides a new research perspective for other researchers to solve traffic problems in the future.”
There are some typographic errors in the whole manuscript. Check it. Unfortunately, the language and sentence structures of this manuscript are sometimes incomprehensible. The paper contains many stale sentences and inappropriate verbs, so it needs thorough rewriting, editing, and language revision to allow for a proper peer review.
Thank you for your valuable advice. Due to our negligence in writing, the use of language cannot meet the requirements of readers. We have asked professional English translators to check the full text, hoping to make readers better understand the idea of the paper.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe answers and modifications are adequate.
This article can be accepted in its current form.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe authors tackled all issues raised by the reviewer.
Reviewer 3 Report
Comments and Suggestions for AuthorsAll the points raised have been addressed satisfactorily. The study is worthy of consideration for publication.
