Urban Traffic Mobility Optimization Model: A Novel Mathematical Approach for Predictive Urban Traffic Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper aims to develop an traffic mobility optimization model, which is a hot and interesting topic. The paper is well organized and written. Howevere, there are some concerns need to be addressed:

(1) Transportation vehicles, such as cars, buses, or bicycles, seem to have not been taken into account in this model. It is an important factor that affects traffic congestion. The author should analyze and explain the reasons for ignoring this factor.

(2) The expressions "j = 3 to 3" and "k = 2 to 2" in Eq (1) are puzzling and need to be explained.

(3)  The authors should explain how to solve this optimization model.

(4) The author used too many subjective words to express the innovation of their work in the writing of the paper, such as "cutting edge", "significant lead forward", "groundbreaking", etc., which are rich in self affirmation tone and should be weakened to make the conclusion more objective and reasonable.

Comments on the Quality of English Language

None

Author Response

Dear Reviewer 1,

Thank you for your valuable insights and suggestions. We appreciate your thorough analysis and acknowledge the need for clarity and depth in our article. Below are our responses to your specific points: Based on the comprehensive feedback provided by Reviewer and ensuring all highlighted statements have been revised accordingly, here is a summary of the adjustments made to the manuscript:

1) In our current model, we considered the average numbers of vehicles such as cars, buses, trucks, and vans. However, we did not include bicycles. The exclusion of these vehicle types in our initial model is due to limitations in data availability, the complexity of modeling, and the scope of the study. We have added an explanation regarding this matter in the conclusion section of our paper. In future research, we aim to incorporate bicycles and other such transportation modes to enhance the accuracy of our model and provide a more comprehensive understanding of urban traffic dynamics. This will enable us to offer more effective solutions for traffic management and urban planning strategies.

2) We apologize for the confusion caused by the expressions "j = 3 to 3" and "k = 2 to 2" in Equation (1). These were errors, and the correct expressions should be "j = 1 to 1" and "k = 1 to 1." We have corrected these notations in the revised manuscript. The corrected expressions now accurately represent the specific time intervals and operational conditions considered in our model.

3) We solved the optimization model using the GAMS/BARON software. This approach allowed us to effectively handle the complexity of the model and obtain precise solutions. We have added a brief explanation of this process to the manuscript.

4) Thank you for your valuable feedback. We solved the optimization model using the GAMS/BARON software. This approach allowed us to effectively handle the complexity of the model and obtain precise solutions. We have added a brief explanation of this process to the manuscript.

5) Subjective Language: We have revised the language throughout the paper to remove subjective terms such as "cutting-edge," "significant leap forward," "groundbreaking," etc., and replaced them with more objective and neutral expressions. For example:
 Original: "This research introduces the UTMOM, a cutting-edge, data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of sophisticated data mining and mathematical modeling."
 Revised: "This research introduces the UTMOM, a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling."
 Original: "This research is propelled by the belief that a nuanced understanding and effective addressing of Ankara's transportation complexities require the integration of cutting-edge data analytics and computational intelligence."
 Revised: "This research is driven by the belief that a nuanced understanding and effective addressing of Ankara's transportation complexities require the integration of advanced data analytics and computational intelligence."
 Original: "This paper provides an in-depth review of cutting-edge research on leveraging deep learning for intelligent traffic management, demonstrating its potential to transform urban mobility."
 Revised: "This paper provides an in-depth review of recent research on leveraging deep learning for intelligent traffic management, demonstrating its potential to transform urban mobility."
 Original: "The study detailed in this paper marks a significant leap forward in comprehending and analyzing urban traffic flow, facilitated by the deployment of UTMOM."
 Revised: "The study detailed in this paper represents an important advancement in comprehending and analyzing urban traffic flow, facilitated by the deployment of UTMOM."
 Original: "Such an investigation has the potential to catalyze the development of groundbreaking and sustainable transportation solutions, significantly contributing to the alleviation of traffic problems."
 Revised: "Such an investigation has the potential to catalyze the development of innovative and sustainable transportation solutions, significantly contributing to the alleviation of traffic problems."
 Original: "A key innovation introduced in our analysis is the strategic weighting system, a groundbreaking technique designed to refine the accuracy of traffic predictions, ensuring a close alignment between UTMOM’s forecasts and the actual observed data."
 Revised: "A key innovation introduced in our analysis is the strategic weighting system, an advanced technique designed to refine the accuracy of traffic predictions, ensuring a close alignment between UTMOM’s forecasts and the actual observed data."

These revisions aim to provide clarity on the innovative aspects of our work and how the results support our claims, addressing the concerns raised by you. By directing readers to the evidence within the manuscript, we believe we have strengthened the manuscript and made the innovative contributions of our research more explicit.

We deeply appreciate the time and effort you have dedicated to reviewing our manuscript. Your insightful comments have significantly contributed to enhancing the clarity and depth of our work. We are grateful for your thorough analysis and valuable guidance throughout this revision process.  Please see the attachment.

Sincerely

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study formulated a model to forecast the daily traffic dynamics, which aims to provide data-informed strategy to optimize the urban traffic issues. It's a classic story with increasing popularity to be studied. However, there are several issues to be further considered concerning this study.

(1) The literature review was not performed well. More important studies should be incorporated to provide a holistic and systematic view.

(2) The methodology is ambiguous to some extent. The formulation and interpretation of the traffic flow prediction issue should be explained further. 

(3) Since the authors have introduced a lot of indices, the dataset for training and validation seems to be limited. How could the authors confirm that the data size is sufficient for modelling?

(4) The validation part was not well explained. Has the dataset been separated into different parts for training and validation？

(5) The advantages of this new model should be compared with those complex non-linear models. For instance, what is the advantage of the proposed model compared with machine-learning based models (which is also highly nonlinear)?

(6) More graphics should be drawn to provide explicit views of the model formulation and modelling results.

Author Response

Dear Reviewer 2,

Thank you for your valuable insights and suggestions. We appreciate your thorough analysis and acknowledge the need for clarity and depth in our article. Below are our responses to your specific points: Based on the comprehensive feedback provided by Reviewer and ensuring all highlighted statements have been revised accordingly, here is a summary of the adjustments made to the manuscript:

1) We appreciate your suggestion to incorporate more important studies to provide a holistic and systematic view. Based on your comments, We have revised the literature review to include a broader range of significant studies and structured it under three main headings: Traffic Data Analysis and Prediction, Mathematical Modeling, and Intelligent Transportation Systems. This approach aims to offer a more comprehensive overview of the current research in these areas.

2) We have revised the methodology section to address your concerns. Specifically, we have enhanced the clarity and detail of the objective function and constraints. We now provide a more thorough explanation of how the objective function minimizes discrepancies between actual and estimated vehicle counts, ensuring alignment with observed data through a weighted sum of squares approach. Additionally, we clarified the selection and justification of weights, emphasizing their empirical basis and importance. We also detailed the constraints used to maintain data consistency and limit variability between consecutive time intervals, thereby preserving the integrity of the traffic flow analysis. These revisions aim to enhance the model's credibility and applicability by customizing the variables and objective function to the specific characteristics of the dataset. We believe these changes significantly improve the comprehensibility and robustness of our methodology.

3) To address your concerns, we have implemented various strategies and explained them in detail in section 4.2 "Dataset Adequacy and Validation" of our article. These strategies include dataset augmentation, cross-validation, empirical analysis, incremental training, and statistical validation. These measures ensure that our dataset is adequate for the complexity of our model and that our findings are both reliable and applicable. We believe these improvements adequately address your concerns and increase the robustness of our work.

4) Thank you for your valuable feedback regarding the validation part of our study. In response to your concerns, we have added detailed explanations in the "Application" section of our manuscript. Specifically, we included new subsections on "Dataset Adequacy and Validation" and "Validation Process." These sections describe how the dataset was separated into distinct parts for training (70%) and validation (30%), and the use of k-fold cross-validation to ensure robustness. We also implemented incremental training and conducted statistical tests to confirm the dataset's sufficiency and representativeness. We believe these additions address your concerns and enhance the clarity and robustness of our study.

5) we have added a section titled "4.9. Comparison with Complex Non-Linear Models" to the manuscript. This section highlights the advantages of our proposed model (UTMOM) over complex non-linear models, including those based on machine learning. We discuss key aspects such as interpretability, data requirements, computational efficiency, customization, robustness, and actionable insights. We believe this addition addresses your concerns and provides a clear comparison of the strengths of our model.

6) We have added more graphical representations to enhance the clarity of our model's formulation and results. Specifically, we have included a new graphic as Figure 3, which compares the vehicle counts for each interval (Morning, Afternoon, Evening) per day, showing both observed and estimated values for Dataset 1 and Dataset 2. This addition provides a clear and explicit view of the model's performance and results.

These revisions aim to provide clarity on the innovative aspects of our work and how the results support our claims, addressing the concerns raised by you. By directing readers to the evidence within the manuscript, we believe we have strengthened the manuscript and made the innovative contributions of our research more explicit.

We deeply appreciate the time and effort you have dedicated to reviewing our manuscript. Your insightful comments have significantly contributed to enhancing the clarity and depth of our work. We are grateful for your thorough analysis and valuable guidance throughout this revision process. Please see the attachment.

Sincerely

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Paper's Contributions:

The paper proposed a method that integrates multiple indicators and prediction targets for traffic flow forecasting,according the differences across datasets.

Some issues need to be modified:

1> The introduction section highlights exciting results try to use Ankara as a case Urban. However, the provided field data is insufficient, that covers only 5 days in the LIMAK Cement Factory area. This misalignment with the actual work is also same in the conclusion, the paper claims lack enough  content supporting, notably in sections 3 ("Adaptability and Flexibility"), 4 ("Strategic Planning Support"), and 6 ("Dynamic Response to Urban Changes"). 

2> The figures and tables in the paper need improvement. Figure 1's purpose is insignificant; the UTMOM is vital, but the paper  lacks a clear diagram to explain its structure. Figure 2's coordinate system and scale make too much confusing, because of the differences and the origin data within a single diagram . Additionally, if the Tables 2 and 3 consolidate daily data into single rows, using columns to distinguish data items, the tables will became simple and clearly.

3> The literature review of this paper is very long, but the number of documents used is not large, and the table of the literature review has little relationship with the subsequent discussion. Specifically,There is no review of the Gaussian regression method, but it is used in the comparison later in the paper.

The method proposed in the paper is not verified using good data. The description of UTMOM should be strengthened. The introduction and conclusion of the paper should match the content of the paper.

 

Author Response

Dear Reviewer 3

Thank you for your valuable insights and suggestions. We appreciate your thorough analysis and acknowledge the need for clarity and depth in our article. Below are our responses to your specific points: Based on the comprehensive feedback provided by Reviewer and ensuring all highlighted statements have been revised accordingly, here is a summary of the adjustments made to the manuscript:

1) We have revised the introduction to better align with the scope of our study and clarified the limitations of the field data. The introduction, methodology, and conclusions now more accurately reflect the data and findings. We have added more detailed explanations and additional data to strengthen the content in these sections. We believe these revisions have significantly improved the manuscript and look forward to your further feedback.

2) We have carefully considered your comments and made several revisions to improve the clarity and quality of our paper.
 Figure 1: We have revised Figure 1 to provide a more detailed and clear diagram explaining the structure of the Urban Traffic Mobility Optimization Model (UTMOM). The updated figure now emphasizes the vital components and workflow of UTMOM, ensuring that its significance and function are clearly illustrated.
 Figure 2: We acknowledge that the original Figure 2's coordinate system and scale were confusing due to the differences and origin data within a single diagram. To address this, we have included a detailed explanation to accompany the revised Figure 2. The new figure utilizes separate Y-axes for vehicle counts and absolute differences, distinct colors, and markers to differentiate between observed and estimated counts, and clear legends and labels for better readability.
 Tables 2 and 3: In response to your suggestion, we have consolidated the daily data into single rows in Tables 2 and 3, using columns to distinguish data items. This restructuring simplifies the tables and makes them clearer and easier to understand.

3) In response, we have enhanced the literature review by adding new studies and statistical analyses, providing a more comprehensive context. We have included a thorough review of the Statistical and regression method to ensure a clear connection to the later comparison in the paper. Additionally, we have updated the literature review table to better reflect the relationships between the reviewed studies and our discussion. We believe these revisions address your concerns and improve the clarity and relevance of our paper.

4) In response, we have strengthened the description of UTMOM and verified our method using more robust data. Additionally, we have revised the introduction and conclusion to ensure they better match the content of the paper. We believe these changes address your concerns and enhance the overall quality of our manuscript.

These revisions aim to provide clarity on the innovative aspects of our work and how the results support our claims, addressing the concerns raised by you. By directing readers to the evidence within the manuscript, we believe we have strengthened the manuscript and made the innovative contributions of our research more explicit. We deeply appreciate the time and effort you have dedicated to reviewing our manuscript. Your insightful comments have significantly contributed to enhancing the clarity and depth of our work. We are grateful for your thorough analysis and valuable guidance throughout this revision process. Please see the attachment.

Sincerely

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I appreciate the revisions you have made to the paper, including adding new references, providing further explanations for the figures, and recreating the tables. However, I feel that there has been a misunderstanding regarding my comments on Figure 1. The paper should include a figure that elucidates the structure of the model itself, reflecting the relationships between various key variables of the model. The current figure is still a very generic flowchart depicting the process of using the model.

Please provide a more specific figure that illustrates the structure of the model itself, rather than a flowchart depicting the process of using the model. This will help clarify the principles of the model and the relationships between its key variables. Thank you for your attention to this matter.

 

Author Response

Dear Reviewer,

Thank you for your detailed feedback on our manuscript. We appreciate your insights and have made the necessary revisions to address your concerns regarding Figure 1. In the original submission, Figure 1 was presented as a generic flowchart depicting the process of using the UTMOM model. Based on your valuable comments, we have significantly revised Figure 1 to elucidate the structure of the model itself, reflecting the relationships between the various key variables.

The new Figure 1 now clearly shows the integration of key variables such as "Time of Day," "Spatial Variations," "Weather Conditions," "Special Events," "Socio-Economic Factors," and "Traffic Regulations and Policies" into the UTMOM model. This detailed flowchart illustrates how these inputs feed into the model, which processes them to generate "Traffic Flow Predictions." This visual representation offers a clear and systematic overview of our methodology, guiding the reader through each step and demonstrating the model's comprehensive approach to urban traffic analysis. 

We believe this revision provides a more specific and accurate depiction of the UTMOM model, enhancing the clarity and depth of our methodological framework as per your suggestion. Thank you again for your constructive feedback. We hope the revised figure meets your expectations and look forward to your further comments.

Best regards

P.S. Please see the attachment. Please note that the corrections made in the attached document are indicated in red font.

Author Response File: Author Response.pdf