-->PONE-D-26-06596-->-->A Study on the Car-Following Model for Mountainous Curves Incorporating Driving Behaviour Characteristics-->-->PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions-->

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Partly

Reviewer #2: Partly

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: No

Reviewer #2: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: General Comments

This manuscript focuses on the critical issue of high accident rates on mountainous highway curves, and proposes an improved Intelligent Driving Model (IDM) that integrates driving behaviour heterogeneity and curve radius parameters. The authors collected real-world vehicle trajectory data via drones, conducted driving style classification using a DE-optimised K-Means algorithm, and calibrated and validated the model for different driving styles. The research topic aligns with the practical engineering needs of mountainous highway traffic safety, and holds certain theoretical significance and application potential for micro traffic flow simulation and intelligent driving system design for mountainous roads. However, there are several key issues in the manuscript regarding the demonstration of research novelty, mechanistic explanation of the improved model, rigour of conclusions, and writing standardisation, which require major revisions before further consideration for publication.

Major Comments

Comment 1: Further optimise the demonstration of research novelty and clearly define incremental contributions relative to existing literature

While the manuscript claims to "couple driving behaviour characteristics with curve radius into the IDM", existing studies have already developed car-following models that take curve-related factors into account, such as driver visual angle on curves, curve alignment, and complex road geometric conditions. Therefore, the manuscript must clearly and explicitly answer the following core questions to highlight its innovations:

1.What is the new theoretical or methodological increment of this study compared with existing curve-related car-following models?

2.What are the specific advantages of the proposed model over existing car-following models that consider driving style heterogeneity?

3.Is the combination of "driving style classification + curve radius correction" proposed in this study a methodological innovation, scenario innovation, or data application innovation? The manuscript needs to make a clear definition and sufficient argumentation.

Comment 2: Supplement the mechanistic explanation of the improved IDM and the clarification of the physical meaning of parameters

The manuscript states that "a curve radius parameter is introduced to correct the desired speed", but lacks sufficient and rigorous explanation for the core modification logic, which is critical to support the scientific validity of the improved model. The following contents must be supplemented in the revised manuscript:

1.The specific action mechanism of curve radius on the desired speed and acceleration terms of the model, including the complete logical chain of how the curve geometric characteristics are mapped to the driver's car-following decision-making process.

2.The theoretical basis for the functional form of the curve radius correction term. It is necessary to clarify whether this form is derived from vehicle dynamics theory, driving perception mechanism, or empirical assumptions, and provide corresponding theoretical derivation or literature support.

3.Whether there is an interaction effect between the curve radius parameter and driving style parameters, and how this interaction is reflected in the model and verified by empirical data.

Comment 3: Avoid over-extrapolation of conclusions and clarify the applicability boundaries of the model

The empirical data of this study are collected from a single typical mountainous curved section in Yunnan Province, which provides a real case for the research, but also brings obvious limitations to the generalisability of the model. The current model may only be applicable to scenarios under "specific road sections, specific traffic composition, and specific environmental conditions", and its extrapolation performance is still limited. For mountainous roads, there may be significant differences in curve radius and gradient combinations, longitudinal gradient conditions, traffic flow composition, traffic control measures, and marking and signage conditions between different sections. The following revisions are required:

1.Clearly define the applicability boundaries of the proposed model in the Discussion or Conclusion section, and objectively explain the applicable scenarios and constraints of the model.

2.Supplement a detailed description of the sample distribution in the Data Collection and Processing section, including the proportion of vehicle types, traffic flow states (free flow, car-following flow, congested flow), and time period distribution of the collected trajectory data.

3.Add a dedicated subsection of Research Limitations in the Conclusion section, explicitly state the limitations brought by the single-section sample, and propose a clear multi-scenario verification plan for future research.

Minor Comments

Comment 4: Further improve the writing standardisation and terminology accuracy of the manuscript

The manuscript has a basic research framework, but there are still many non-standard issues in writing and expression that need to be revised and improved:

1.Unify the core terminology related to car-following behaviour. The manuscript alternately uses "tailgating behaviour", "following behaviour" and "car-following behaviour", which causes ambiguity. It is recommended to uniformly use the standard academic term "car-following behaviour" in the full text, and only use "tailgating behaviour" when specifically referring to dangerous close following behaviour.

2.Standardise the presentation of figures, tables, variables, units, and symbol annotations. For example, correct the incomplete table titles (such as Table 8), inconsistent figure numbering formats, typographical errors in parameter units in tables, and incomplete explanations of formula symbols, to ensure that all variables and units have clear and consistent physical meaning explanations in the full text.

3.Enrich and standardise the description of research limitations and future research directions in the Conclusion section. The current relevant content is too brief and vague, and needs to be expanded into a structured and specific narrative combined with the actual work of this study.

Reviewer #2:  1. Supplement the geometric parameters of curves in the study area, technical parameters of UAV data collection, and verification data of trajectory extraction accuracy; improve on-site real-scene photos of the experiment and complete all experimental elements.

2. Add inter-group significance tests between clustering validity evaluation indicators and driving behavior parameters to enhance the statistical rigor and scientificity of data analysis.

3. Derive the coupling theory between curve radius and the IDM model, and improve the explanation of the physical meaning of parameters; supplement comparative verification with classic car-following models for horizontal curves and enhance the analysis of model generalization ability.

4. In data processing, use km/h for speed and m/s for other variables; units shall be standardized and unified.

5. In the conclusion section, avoid repetition with the abstract and systematically sort out the research context; propose specific directions for future research based on model limitations, and clarify engineering application scenarios such as mountainous traffic simulation and intelligent driving system design.

6. Enrich the discussion section by integrating the latest research findings in driving behavior modeling and road safety evaluation; supplement citations of relevant high-quality literature to strengthen the theoretical support of the study. A real-time conflict risk prediction modeling based on causal inference and graph-model: Applied to multi-configuration expressway diversion zones. Transportation Research Part C: Emerging Technologies, 2025, 178, 105219. https://doi.org/10.1016/j.trc.2025.105219

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Reviewer #1: No

Reviewer #2: No

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A Study on the Car-Following Model for Mountainous Curves Incorporating Driving Behavior Characteristics

PONE-D-26-06596R1

Dear Dr. Zhang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zhihong (Arry) Yao, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #2: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #2: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

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-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #2: Yes

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-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #2: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #2: Compared with the original manuscript, the authors have made sufficient revisions and improvements. To further enhance the quality of the paper, the specific comments are proposed as follows:

1. The authors need to enhance the description of the rationality of the clustering results. In Section 4.1.1 "Analysis of Clustering Results", the number of clusters is preset to 3. Although I agree with this practice, a more scientific approach would be to compare the clustering results with different numbers of categories. Therefore, the authors need to explicitly clarify the rationality of presetting the number to 3. It is recommended to refer to: Da Xu, NengChao Lyu, Liu Yang. A Proactive Risk Prediction Framework for Cut-In Maneuvers Incorporating Inherent Driving Styles[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(12), 23084-23099.

2. Information on the proportion of samples after clustering is missing. After conducting the sample clustering, the authors did not introduce the quantity and proportion of the three types of samples. This is very important information, which determines the rationality of the clustering algorithm.

3. The standardization of figures and tables needs to be further enhanced. For example, the text in Figure 1 and Figure 13 is illegible, and Figure 14 lacks horizontal and vertical axis labels.

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Reviewer #2: No

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