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Article
Peer-Review Record

Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System

Sustainability 2022, 14(23), 16322; https://doi.org/10.3390/su142316322
by Vandana Singh, Sudip Kumar Sahana * and Vandana Bhattacharjee
Reviewer 3: Anonymous
Sustainability 2022, 14(23), 16322; https://doi.org/10.3390/su142316322
Submission received: 8 September 2022 / Revised: 26 November 2022 / Accepted: 3 December 2022 / Published: 6 December 2022
(This article belongs to the Special Issue Control System for Sustainable Urban Mobility)

Round 1

Reviewer 1 Report

The English is very poor (It seems that the paper is not reviewed by the authors). Many typos, grammatical and language structure problems exist in the manuscript (example first word in the Abstract).

Capitalization must be consistent and uniform throughout the paper. Example Cloud-Crowd Computing based intelligent transportation system (CCCITS).

Many sentences are unclear and very hard to understand, sometimes ambiguous. Examples:

… communication technologies at every place and ITS (Intelligent Transportation system) has been the one.

Now, when vehicle strength increases, this causes numerous problems …

No doubt, we need to brake on accidents that occur on roads ….

through optimizing techniques along ….

As an example, vehicle drivers get quick alerts about the road conditions and if such are clear or if some unpleasant event has taken place somewhere due to which they need to opt for some safer route to reach their destination safely.

Several claims/sentences are scientifically not correct. Examples

              Cloud computing is a media framework to check the security issues and as it is easily
accessible to all.

In addition to that, the paper lacks the good presentation style such as indentation, line brakes in the middle of a sentence (page 2, 4 for example), very long sentences, etc.

The Contributions of the manuscript-at-hand should be documented in a much more categorical manner, i.e., towards the end of the Introduction as (1) ... , (2) ... , and (3) ... , and so on.

A Comparative Table in Seciton 2, Related Works, delineating the pros and cons of the referred approaches would be appreciated.

The literature review is not written critically. It is not clear from the discussion what limitations exist in the present literature. Without this being done, the gap cannot be identified, and therefore, the motivation of the work is not clear.

The references cited in this paper are incomplete. The authors should also mention the relevant research in the recent literature. Examples:

https://www.hindawi.com/journals/jat/2021/4037533/ 

             https://ieeexplore.ieee.org/abstract/document/9768186/

             https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=20888708&AN=158378750&h=hoVK5mHMauE8pvQRI9d8C6BFc4EdwKbonZRHb1Mz%2FwvrVk37ne6WEhbpKs5KVx6nZJEEcLN4HHcozfkVabFaMg%3D%3D&crl=c

             https://link.springer.com/chapter/10.1007/978-981-19-1677-9_18

             https://ieeexplore.ieee.org/abstract/document/9857617

 

 

The references are outdated (most of the references are dated 2019 with only one exception 2020). In these very hot research topics, usually, hundreds of related papers have been published since 2020. It seems that the authors are unaware of the huge advancement in the topic.

 

Figures are in low resolution. Must be in vector format or having HR format.

The results presented in Table 1 and Figure 6 must be explained in more detail. It is not clear what is iteration (x axis stands for); even in the text, it is not clear. Is it the training epoch? I

 

I cannot understand why the departed vehicles (without GA or PSO or ACO) increase with the iteration. 

Author Response

Respected Reviewer, We thank you very much for the comments and suggestions.

Author Response File: Author Response.docx

Reviewer 2 Report

Authors have presented their work titled “Nature Inspired Cloud-Crowd Computing for Intelligent Transportation System”. In this work, Considering the day-to-day ideal traffic light cycle problem, the CCCITS model is proposed where the model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and crowd-sourcing incidental sentiment. The results presented in this work finds be novel to my observation. But few places in the manuscript seems to be weak , hence I am suggesting the following points to consider :

 

1.   Literature review sections is weak. Authors are suggested to add few more recent works and at least 3 works under each area of interest should be made available.

2.    Figure 2 is very small which is difficult to read.

3.   Equations shall be numbered and same to be quoted inside text.

4.   Experimental results are weak. Consider adding few more results

5.   Authors are suggested to strongly compare their method with state of the art methods available ( same should be made available in literature review section)

 

With these suggestions, I recommend for publication. 

Author Response

Respected Reviewer, We thank you very much for the comments and suggestions.

Author Response File: Author Response.docx

Reviewer 3 Report

The authors optimized signal timings using a cloud-computing approach and nature-inspired algorithms. Overall, this study needs improvement. First, the problem statement needs to be clearly identified, and as such, it needs to be derived based on an extensive literature review (that will contain a table that summarizes previous studies and how the proposed study differs from the others). In the Experimental Setup section, please provide more information about the size of the network and characteristics of the network, and the amount of data collected. In Results and Analysis, it is necessary to document the benefits of the proposed method on several spatial levels (intersection level analysis, corridor level, and whole network), using different performance measures (delay, number of stops) to understand the efficiency of the proposed solutions better. Lastly, authors should discuss differences in signal timing parameters (green, cycle length, offset – if optimized) before and after optimization. Some more specific suggestions are given below:

 1. Introduction

"Although these techniques have proven to be effective in a number of cities throughout the world, real-time traffic network management has a very high functioning cost, and traffic flow patterns in the real world tend to repeat themselves (peak hour, holidays, etc.).”

Please support this statement with the following reference:

·         Dobrota, N., Stevanovic, A., & Mitrovic, N. (2020). Development of assessment tool and overview of adaptive traffic control deployments in the US. Transportation research record, 2674(12), 464-480.

2. Literature Review

“With the exception of [19], Prior work only used queue length information to control signals.”

In addition to cited work, please support the statement with recent relevant studies as well:

·         Dobrota, N., Stevanovic, A., & Mitrovic, N. (2022). Modifying signal retiming procedures and policies by utilizing high-fidelity modeling with medium-resolution traffic data. Transportation research record, 2676(3), 660-684.

Also, please review the following relevant study that uses Bee-Colony Optimization Approach to develop signal timings:

·         Jovanović, A., Stevanović, A., Dobrota, N., & Teodorović, D. (2022). Ecology based network traffic control: A bee colony optimization approach. Engineering Applications of Artificial Intelligence, 115, 105262.

 

5.3 Results and Analysis
“NIA such as GA, ACO and PSO algorithm have been developed for traffic signal optimization
techniques for creating successful traffic light programs for an area in Ranchi, Jharkhand
(India).”

The algorithms were used, not developed, so please revise this statement.

“It is observed the 24% of improvement can be achieved using proposed PSO and 22% and 17% of improvement have been achieved using ACO and GA respectively.”

 

Improvement of what? Please be more specific on which performance measure was used to evaluate the quality of solutions. Additionally, for your evaluations, provide changes in the following commonly used traffic signal performance measures, such as delay, number of stops, and travel time.

Author Response

Respected Reviewer, We thank you very much for the comments and suggestions.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

I went through the manuscript. It is significantly improved compared to the previous version. However, it has to be proofread to solve several issues in English.

 

Author Response

Respected reviewer, 

As you suggested, we have improved the grammar and unclear sentences using grammar editing software(Grammarly paid version).

Reviewer 3 Report

The authors didn’t substantially improve manuscript especially in the parts  regarding to methodology, experimental setup, results section, and hence paper should not be accepted.

Author Response

Respected Reviewer,

we thank you so much for your response 

the objective of this research is to increase the number of departed vehicles which informs a good flow of traffic in the network. Flow traffic in the network informs less waiting time and reduces travel time. A simulation run on the same scenario using a conventional algorithm (Dijkstra) has been done to evaluate the performance comparison with NIA algorithms. Also, an instance is taken from real road traffic to compare the performance of considered NIA algorithms with real-time road traffic scenarios. The departed vehicle increase with iteration in the previous version indicated the scenario using a conventional algorithm. In the current version, “Departed vehicle” is modified and denoted as “Departed vehicle using the conventional algorithm”. The flow of vehicles in real-world traffic at that instance was 326.

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