Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The author needs to give a bit more clarity about the results of this research in this abstract section

2. What does DBSCAN stand for? Please give a short description of this clustering method in the introduction section.

3. You must provide a short introduction to DPAIA in this section. Why do you choose DPAIA to customise the bus stop location model?

4. You must state clearly the difference between a cross-product and a dot product. Usually, we use . (dot) as an operator for multiplication rather than x (cross)

5. Please rewrite some formulas using the proper math type editor or equations.

Author Response

1. The author needs to give a bit more clarity about the results of this research in this abstract section

Thanks for your advice. We have expanded the results section of the abstract based on the suggestion provided, such as “Through simulations with Chengdu ride-hailing data, DPAIA algorithm minimized the weighted cost to 28.95 ten thousand yuan, outperforming all counterparts. Though proposing 9-11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban con-gestion while boosting operator profits.”

 

2. What does DBSCAN stand for? Please give a short description of this clustering method in the introduction section.

Thanks for your advice. Based on your suggestion, we have added a brief explanation of the DBSCAN algorithm in the introduction section. Such as “Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a prominent data clustering algorithm that stands out for its ability to discover clusters of arbitrary shapes in spatial databases [10]. Unlike traditional clustering methods such as k-means, which require the number of clusters to be predefined, DBSCAN identifies clusters based on density, automatically determining the number of clusters based on the data's inherent structure.”

 

3. You must provide a short introduction to DPAlA in this section. Why do you choose DPAlA to customize the bus stop location model?

Thanks for your advice. We have introduced the DPAlA algorithm in the section 4.1. Principle of Dual Population Adaptive Immunity Algorithm (DPAIA). In addition, we have explained that customizing the bus stop location model tends to find the optimal solution to the problem with a high probability. Then DPAIA algorithm possesses advantages such as global convergence, parallelism, and adaptability. IA has strong global search capabilities, robustness, and a parallel dis-tributed search mechanism[11], making it suitable for solving the stop model.

 

4. You must state clearly the difference between a cross-product and a dot product. Usually, we use .(dot)as an operator for multiplication rather than x(cross)

Thanks for your advice. Based on your suggestion, we have reviewed the literature and corrected Equation 16. Please refer to the modifications mentioned in the manuscript.

 

5.Please rewrite some formulas using the proper math type editor or equations.

Thanks for your advice. We have rewrite the formulas in the proper math type.

Reviewer 2 Report

Comments and Suggestions for Authors

1. In Table 2 pg 9.

    Point 6. Which are formulas (1.12) and (1.13)

    Point 14. Which are formulas (1.9) and (1.10) in this paper

2. Can a flow chart be made for completing this model

3. In Fig. 6. It is best to add information or explanation regarding the  2 colors

    red and green

4. In general, in the presentation og this paper there are many redundant 

    explanations

5. Can the presentation of the abstract and conclusion be made to

    differentiate more clearly between the abstract and the conclusion

 

 

 

Author Response

1. In Table 2 pg 9.Comments andSuggestions for Authors

Point 6.Which are formulas (1.12)and (1.13)

Point 14.Which are formulas (1.9) and (1.10)in this paper

Thanks for your advice. Thank you for your suggestions. We have made revisions and corrections based on your feedback. To facilitate your review and confirmation of these changes, we have highlighted all adjustments and modifications in red within the manuscript.

 

2.Can a flow chart be made for completing this mode

Thanks for your advice. Based on your feedback, we have thoroughly revised and refined the algorithm's flowchart. As shown in Figure 1, the updated flowchart now includes more detailed steps and decision points to clearly illustrate the entire algorithm's operation process.

 

3.In Fig.6 lt is best to add information or explanation regarding the 2 colors red and green.

Thanks for your advice. In Figure 6, the red dots and green dots represent specific meanings, which we have already explained. The red dots signify bus stop positions, while the green dots signify he passenger positions.

 

4.In general, in the presentation og this paper there are many red undant explanations.

Thanks for your advice. Based on your suggestions, we have simplified the presentation and the modify details can be seen in the revised version.

 

5.Can the presentation of the abstract and conclusion be made to differentiate more clearly between the abstract and the conclusion.

Thanks for your advice. Based on your feedback, we have thoroughly revised the conclusion. Such as “In this paper, we tackled the problem of customized bus stop location by developing a novel model based on the immune algorithm. Our approach integrates "data analysis + mathematical modeling + solution" to achieve a balanced outcome that ad-dresses the needs of both passengers and operators. Specifically, we introduced the Dual Population Adaptive Immunity Algorithm (DPAIA) to solve this model. By en-hancing the traditional immune algorithm with adaptive crossover and mutation rates, we effectively reduced computational costs. Additionally, the introduction of an in-vading population, which is periodically combined with the main population, ensured diversity and prevented the algorithm from converging prematurely on local optima. Our results demonstrate that the DPAIA successfully identifies bus stop locations that minimize the weighted sum of passengers' walking distances and the operator's eco-nomic expenditures, offering a robust solution to the customized bus stop location problem.”

Author Response File: Author Response.docx