Applications of Artificial Intelligence in Transport: An Overview
Round 1
Reviewer 1 Report
This paper presents an overview of the applications of AI to a variety of transport-related problems. The range of applications is expected to increase as our cities and transport systems become more instrumented providing much needed data for AI application development. The review focused on a number of application areas which are expected to have more influence in future cities including autonomous vehicles, public transport, disruptive urban mobility, automated incident detection, future traffic status prediction, and traffic management and control. It shows that AI can be used to solve the challenge of increasing travel demand, CO2 emissions, safety concerns, and wasted fuels. My comments are as follows: 1- The main part of such papers it the current challenging and future works. The section for this part should be expanded significantly 2- There are many evolutionary NNs too that are worthy of discussion. 3- I also recommend including the computation complexity analysis of the algorithms. This gives readers an idea how expensive these algorithms are. 4- The paper reviews some meta-heretics such as SA, GA, and BCO. But what about other recent similar algorithms that have been highly cited and proved to be more efficient?
Author Response
Dear editor
Trust our message finds you well. First, thank you and the two astute reviewers for their extensive, meticulous, insightful and constructive comments. Second, thank you for providing an opportunity to revise the paper.
We did our best to address all the comments. Below is a summary of the changes
1- “The limitation of AI techniques” is expanded in section 3 including communication and device (sensor/actuator) side limitations on AI (analytics) as the reviewers suggested.
2- We added “Computation Complexity of AI algorithms” as part of section 3 based on the reviewers comments.
3- I have added more AI algorithms such as, SVM, Decision tree, K-nearest neighbours.
4- I also added Map Reduce solution to ease computational complexity of AI algorithms
5- More recent AI optimisation technique was added such as Fuzzy Logic Model.
We are providing the revised paper with all changes highlighted as well as point-to-point responses to the comments is attached in a word file below.
We trust the paper now meets your high publication standard
Yours faithfully,
Rusul
Author Response File: 
Author Response.docx
Reviewer 2 Report
1. Some typos, "Application of AI inAviation", some grammar errors; "In 2010, Google has presented ..."
2. A few machine learning techniques that are used in IoT scenarios including transport are missing; decision trees, vector machines
3. No mention of interaction between big data and ANN including distributed computing via MapReduce
4. No mention of the communication and device (sensor/actuator) side limitations on AI (analytics).
Author Response
Dear editor
Trust our message finds you well. First, thank you and the two astute reviewers for their extensive, meticulous, insightful and constructive comments. Second, thank you for providing an opportunity to revise the paper.
We did our best to address all the comments. Below is a summary of the changes:
1- “The limitation of AI techniques” is expanded in section 3 including communication and device (sensor/actuator) side limitations on AI (analytics) as the reviewers suggested.
2- We added “Computation Complexity of AI algorithms” as part of section 3 based on the reviewers comments.
3- I have added more AI algorithms such as, SVM, Decision tree, K-nearest neighbours.
4- I also added Map Reduce solution to ease computational complexity of AI algorithms
5- More recent AI optimisation technique was added such as Fuzzy Logic Model.
We are providing the revised paper with all changes highlighted as well as point-to-point responses to the comments attached as a word file below.
We trust the paper now meets your high publication standard
Yours faithfully,
Rusul
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
My comments have been addressed.
Author Response
The authors would like to thank the reviewers and Editor for their valuable comments and suggestions which will help improve the quality of this paper. We have revised the paper to take all suggestions into account. In our revised submission, we have highlighted in green where new text and changes have been made. Additional references that take into consideration the Editor’s request to cover more areas where AI can be applied have also been highlighted in green. The authors value the time and effort spent by the reviewers and the Editor in reading this paper and providing us with these suggestions, and hope the revisions meet your requirements.
Author Response File: Author Response.docx
