Smart Mobility with Big Data: Approaches, Applications, and Challenges

Round 1

Reviewer 1 Report

This paper presents the survey on Bigdata use in smart applications. It claims that the paper studied 92 papers with 90 are between 2015 and 2022. But very few are studied in the years 2021, 2022 and 2023. The survey also does not talk about the results. It just presents the approach used. The topic is of no interest and the presentation is underwhelming.

The paper just presents a superficial analysis and not a deeper analysis. 

The paper is readable and easy to understand. But the results and the contents are not studied or presented deeper.

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript appears to be focused on the analysis of smart mobility big data and its application in addressing future mobility challenges. The authors highlight the importance of big data analysis in identifying and solving problems related to smart mobility, such as personal information leakage and data visualization. They discuss the use of machine learning and big data frameworks such as Apache Hadoop, Apache Spark, and Apache Kafka for analyzing and processing large amounts of data generated by smart mobility devices.

 

While the abstract provides a good overview of the main points covered in the manuscript, it would be helpful to have more details on specific applications of Apache Spark Streaming and data warehouses. The authors could include examples of how these technologies can be used to address mobility challenges, as well as any limitations or drawbacks associated with them.

 

Furthermore, the authors could provide more recommendations on how to use big data frameworks in the context of smart mobility. For example, they could suggest best practices for data management, data security, and data privacy in the context of smart mobility. They could also discuss potential future developments in the field of smart mobility and how big data analysis can contribute to these developments.

In the conclusion section, the authors could provide a summary of the main points covered in the paper and emphasize the importance of smart mobility data analysis for addressing future mobility challenges. They could also highlight the potential benefits of using big data frameworks such as Apache Hadoop, Apache Spark, and Apache Kafka for analyzing and processing smart mobility data.

 

To further enhance the conclusion section, the authors could provide some examples of how the research presented in the paper could be extended or applied in real-world scenarios. For example, they could discuss how the analysis of smart mobility data could be used to optimize traffic flow, reduce congestion, or improve public transportation systems. They could also suggest ways in which machine learning algorithms could be used to predict traffic patterns or identify areas where road maintenance is needed.

 

In addition to providing examples, the authors could also suggest areas for future research. For instance, they could propose new applications of big data frameworks or machine learning algorithms in the context of smart mobility, or discuss potential challenges or limitations that need to be addressed in future research.

 

By including examples and suggestions for future research, the authors can help readers better understand the practical implications of their research and inspire further investigation in the field of smart mobility data analysis.

 

Overall, the manuscript appears to be a valuable contribution to the field of smart mobility and big data analysis. By providing more information on Apache Spark Streaming, data warehouses, and other recommendations, the authors could further enhance the manuscript and provide additional insights for readers.

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Reviewer 3 Report

This paper conducted a comprehensive review of approaches to transportation big data, an introduction of smart mobility services and applications of the latest trends, and analyzed the limitations of the current study and directions for future research. I think it’s useful for smart mobility with big data. To improve this paper, I have some suggestions.

1. I suggest the authors to add a discussion section to describe the advantages and disadvantages of the existing machine learning algorithms and big data frameworks.

2. Studies of web service for big data have been developed in past two decades. The author should add some discussions. Some relative references can be added in the paper.

A web service-oriented geoprocessing system for supporting intelligent land cover change detection

Edge computing vs. Cloud computing: an overview of big data challenges and opportunities for large enterprises

Software as a service, Semantic Web, and big data: Theories and applications

Toward a Novel RESTFUL Big Data-Based Urban Traffic Incident Data Web Service for Connected Vehicles

Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Reviewer 4 Report

This article introduces some application problems about smart mobility with big data. The content of this paper has some enlightenment for related researchers. And the expression is also clear and detailed.

However, the article also has some points should be improved. One is the latest literature is not sufficient. For example, in Table 1, there is less literature in the last three years（2021,2022,2023）. For a review, it suggests that the authors should try to survey the most recent literatures.

The other is some introduction of challenges are not satisfied. For example, in open challenges the details of the problems are short. And in this section, no references are cited. It is doubtful whether these ideas are put forward by the author or have been proposed by other researchers.

The expression is clear and detailed.

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Reviewer 5 Report

This paper analyzes in detail the application problems brought about by the increase in data exchange, such as personal information leakage and data visualization, focusing on analysis methods using machine learning and architectural research methods using big data frameworks It is a topic of interest to the researchers in the related areas but the paper needs improvement. My detailed comments are as follows:

1.Figure 1 and 2 are not clear, please resize to a clearer figure.

2.There exist some space error

Eg: On line 27, should be “2023 [1].”

      On line 29 “Section2” should be “Section 2” et al.

      On line 405 “system(MVDS)” should be “system (MVDS)”

3. Transitional sentences need to be added between paragraphs

4. The article is a bit of a technical report and does not address scientific issues, please clarify the research question and research gap.

5. The format of the Table 2 is incorrect and the three-line table should be adopted.

6. The conclusion is not enough and attention should be paid to distinguishing paragraphs.

 

In short, there are still some adjustments needed in the image, format and content of this article.

This paper analyzes in detail the application problems brought about by the increase in data exchange, such as personal information leakage and data visualization, focusing on analysis methods using machine learning and architectural research methods using big data frameworks It is a topic of interest to the researchers in the related areas but the paper needs improvement. My detailed comments are as follows:

1.Figure 1 and 2 are not clear, please resize to a clearer figure.

2.There exist some space error

Eg: On line 27, should be “2023 [1].”

      On line 29 “Section2” should be “Section 2” et al.

      On line 405 “system(MVDS)” should be “system (MVDS)”

3. Transitional sentences need to be added between paragraphs

4. The article is a bit of a technical report and does not address scientific issues, please clarify the research question and research gap.

5. The format of the Table 2 is incorrect and the three-line table should be adopted.

6. The conclusion is not enough and attention should be paid to distinguishing paragraphs.

 

In short, there are still some adjustments needed in the image, format and content of this article.

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Reviewer 6 Report

In this paper, the authors survey smart mobility with big data regarding approaches, applications, and challenges. However, the paper should be clarified as follows.

- In Section 3, the relationship between machine learning and big data framework is unclear. Please explain why they are combined into one section and how the cited papers are classified into one topic. It is much better if, in Section 3.2, the authors analyze which frameworks have been used in papers cited in Section 3.1 instead of introducing new papers.

- The reviewer also cannot find any relationship between Section 3 and Section 4. The paper sets in the two sections are also disjoint. Again, it would be much better if the authors could analyze which frameworks and machine learning approaches the papers in Section 4 used.

- Section 5 presents open challenges in smart mobility with big data, but the reviewer needs help finding evidence for these challenges. For example, "technical limitations" is not a clear challenge because multiple approaches, such as machine learning for vision, digital signal processing with lidar sensors, or connected vehicles' information, may exist concurrently in a particular mobility system. Then, the failure rate when these approaches are combined will be shallow. It would be appreciated if the authors could analyze a full system with multiple techniques instead of considering every single strategy to make conclusions.

- The reviewer agrees with the "legal limitations" challenge.

- Please clarify why "Rapid Technological Development" is a challenge because we can improve our systems with new technology proposed and implemented.

Writing English should be carefully revised. For example, the first sentence in Section 3 is not grammarly correct "This section, discusses how to analyze and store smart mobility big data exploit 50 machine learning algorithms and big data frameworks."

Author Response

Thank you for reviewing the paper.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Nominal improvement has been made in the paper

Reviewer 2 Report

All my comments are taken into account

Author Response

We thank you for kind acceptance.

Reviewer 3 Report

I think its ready for publication.

Author Response

We thank you for your kind acceptance.

Reviewer 5 Report

The authors have satisfactorily addressed most of my concerns. The quality of the manuscript improved a lot. I recommended acceptance after minor revision. But the conclusion part is too short. I have no further comments.

Author Response

We thank you for your comments. According to your comments, we have revised the conclusion section from lines 644 to 647.

Reviewer 6 Report

The revised manuscript is not highlighted new text. Hence, it is really difficult for the reviewer to review the changes.

The responses of the authors are not sufficient enough and do not focus on what the reviewer suggested.

For the first comment: "In Section 3, the relationship between machine learning and big data framework is unclear. Please explain why they are combined into one section and how the cited papers are classified into one topic. It is much better if, in Section 3.2, the authors analyze which frameworks have been used in papers cited in Section 3.1 instead of introducing new papers." => the authors only add a short summary section without any explanations or analysis as required.

For my third comment: "Section 5 presents open challenges in smart mobility with big data, but the reviewer needs help finding evidence for these challenges. For example, "technical limitations" is not a clear challenge because multiple approaches, such as machine learning for vision, digital signal processing with lidar sensors, or connected vehicles' information, may exist concurrently in a particular mobility system. Then, the failure rate when these approaches are combined will be shallow. It would be appreciated if the authors could analyze a full system with multiple techniques instead of considering every single strategy to make conclusions." => the authors do not argue and modify the manuscripts well. I don't see any full system analyzed to demonstrate the challenges the authors mentioned.

My last comment: "Please clarify why "Rapid Technological Development" is a challenge because we can improve our systems with new technology proposed and implemented" is not taken into account.

English is okie.

Author Response

The reply has been attached as a file.

Author Response File: Author Response.docx

Round 3

Reviewer 6 Report

The reviewer agree with the updated version.