Left-Turn Lane Capacity Estimation based on the Vehicle Yielding Maneuver Model to Pedestrians at Signalized Intersections

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.Please refine the abstract to highlight the creative work. The major contribution should be carefully described. 

2 In Section 3-4, a few formulas have been given. The explanation should be added. Why Eqs.(3) and Eq.(5) are feasible?

3. The proposed model has been presented. How to check the efficiency? Case studies should be given to explain it.

4. The structure in Section 4 should be carefully checked and improved.  A data analysis should be given to describe the reasonablility of Eq.(6).

5. The conclusion part should be improved. The practical contributions should be carefully described. The current version should be refined.

 

 

Comments on the Quality of English Language

English should be improved and checked carefully.

Author Response

Thank you so much for the valuable comments and suggestions. Please see the attachment which includes the revisions.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. The model was developed by using data obtained from several pedestrian crossings in Japan. What are the innovative points of the model used in this study?

2. How is the model developed in this research achieved in terms of reducing vehicle-human conflict and reducing air pollution?

3. Please explain the reasons for maintaining traffic flow and enhancing sustainability through methods such as signal optimization and two-section crossing

4. It is suggested to cite the following two papers：

Left-turn Lane Capacity Estimation based on the Vehicle Yielding Maneuver Model to Pedestrians at Signalized  Citation corresponding to Intersections, X. Chen, Z. Wang, Q. Hua, W.-L. Shang, Q. Luo, and K. Yu,  "AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis,"  IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 4541-4552, 2023, doi:  10.1109 / TITS. 2022.3167650.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features Corresponding citations of the paper, X. Chen, M. Wang, J. Ling, H. Wu, B. Wu, and C. Li,  "Ship imaging trajectory extraction via an aggregated you only look once (YOLO) model,"  Engineering Applications of Artificial Intelligence, vol. 130, 2024, doi: 10.1016 / j. ngappai. 2023.107742.

Comments for author File: Comments.pdf

Author Response

Thank you so much for the valuable comments and suggestions. Please see the attachment which includes the revisions.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper studies a left-turn lane capacity estimation problem and proposes a Monte-Carlo simulation-based method. The experimental results look competitive. But I have some suggestions as follows.

1. The correlation between the left-turn lane capacity and air pollution is not very significant. The authors are suggested to reorganize the research background.

2. The contribution of this paper needs to be refined based on differences with the existing literature.

3. The calculation procedures of JSTE and HCM described in Sections 2.1.1 and 2.1.2 are not suitable for the literature review section. If the proposed method borrows from some of these parts, it should be put into Section 3; otherwise, there is no need to show the detailed procedures.

4. The authors are suggested to merge the last paragraph of Section 2.1.3 with its previous paragraph.

5. There is a mistake with the citation. For example, Chen et al. (2008) [16] contain two citation methods.

6. Picture clarity needs to be improved.

7. What do PG, PFG, and G mean in Table 1?

Comments on the Quality of English Language

Please revise and proofread the manuscript to improve its readability.

Author Response

Thank you so much for the valuable comments and suggestions. Please see the attachment which includes the revisions.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Comments were addressed.

Comments on the Quality of English Language

English can be improved.