Operational Evaluation of Mixed Flow on Highways Considering Trucks and Autonomous Vehicles Based on an Improved Car-Following Decision Framework

Response to Reviewer 1 Comments

1. Summary

We sincerely thank you for your positive feedback on the clarity and structure of our manuscript, as well as for your detailed language-related suggestions. In response, we carefully reviewed the entire manuscript and made all recommended grammatical and stylistic corrections to improve readability and consistency. These include refinements to sentence structure, corrections to spelling and word usage, clarification of abbreviations, and improvements to figure labeling and visual presentation. All changes have been clearly highlighted in the revised manuscript using track changes. We greatly appreciate your careful reading, which has helped us further enhance the overall quality and clarity of our work.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

Yes

Are the research design, questions, hypotheses and methods clearly stated?

Yes

Are the arguments and discussion of findings coherent, balanced and compelling?

Yes

For empirical research, are the results clearly presented?

Can be improved

Is the article adequately referenced?

Yes

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Manuscript is well organized and presented clearly.  Only requires minor grammatical corrections.

Response 1: We sincerely appreciate your positive assessment of our manuscript's organization and clarity. As suggested, we have carefully reviewed the entire manuscript and made the necessary grammatical corrections to improve the language quality.

4. Response to Comments on the Quality of English Language

Point 1: Line 46 Capitalize first work of sentence.

Response 1: The suggested correction has been made (Line 47), and the change is highlighted in red in the revised manuscript.

Point 2: Line 92 change importation to important.

Response 2: The word "importation" has been changed to "important" (Line 92) as suggested, and the correction is highlighted in red in the revised manuscript.

Point 3: Line 108 correct spelling or automation.

Response 3: The term 'cellular automaton method' in Line 108 is the standard terminology in traffic modeling, as established in the foundational work by Nagel & Schreckenberg (A Cellular Automaton Model for Freeway Traffic). We have verified the spelling and technical accuracy of this term, and it remains unchanged.

Point 4: Table 2 define dhw, thw, and ttc.

Response 4: We have added the definitions of dhw (distance headway), thw (time headway), and TTC (time to collision) in Table 2 as suggested. 

Point 5: Line 137 spell out Seventy percent.

Response 5: The numerical expression '70%' in Line 137 has been spelled out as 'Seventy percent' as suggested, with this change highlighted in red in the revised manuscript.

Point 6: Line 151 higher than those.

Response 6: The phrase in Line 151 has been revised to 'higher than those' as suggested, with the modification highlighted in red in the revised manuscript.

Point 7: Line 155 delete 43.

Response 7: The '43' in Line 155 has been deleted as suggested.

Point 8: Line 164 HDV passenger car is following.

Response 8: The phrasing 'HDV passenger car is following' has been modified as recommended, with the correction highlighted in red in the revised manuscript for clarity.

Point 9: Line 198 check colors in Figure 4 red is HDV and blue is AV.

Response 9: Figure 4 colors are correct (red: HDV; blue: AV).

Point 10: Line 215 define highlighted abbreviations.

Response 10: All key abbreviations in Line 215 have been bolded and highlighted in red for clarity.

Point 11: Line 219 with the speed that cannot exceed.

Response 11: The phrasing in Line 219 ('with the speed that cannot exceed') has been revised as suggested, with the modification clearly highlighted in red in the revised manuscript.

Point 12: Line 222 define the highlighted abbreviations.

Response 12: All key abbreviations in Line 223 have been bolded and highlighted in red for clarity.

Point 13: Line 271 regardless of vehicle type.

Response 13: The phrase 'regardless of vehicle type' has been modified as recommended, with the correction highlighted in red in the revised manuscript.

Point 14: Line 287 delete the word which.

Response 14: The word 'which' in Line 287 has been deleted as suggested.

Point 15: Line 297 occupies 500 cells.

Response 15: The phrase 'occupies 500 cells' has been revised as suggested, with the modification highlighted in red in the updated manuscript.

Point 16: Line 320 the threshold values.

Response 16: The term 'the threshold values' has been modified as suggested, with the correction highlighted in red in the revised manuscript.

Point 17: Line 327 check spelling of serios.

Response 17: The misspelled word 'serios' has been corrected to 'series (a)' as suggested, with the revision highlighted in red for easy identification.

Point 18: Plots 8.1, 8.2, 8.3 symbols are too small making it difficult to distinguish the different data sets.

Response 18: We improved Figures 8.1-8.3 by adding color differences to compare existing different lines.

Point 19: Line 334 and 342 incorrect figure numbers.

Response 19: The incorrect figure references in Lines 335 and 343 have been updated to the accurate numbering, with all modifications highlighted in red.

Point 20: Line 335 meaning of this sentence is unclear improve grammar meaning of exacted drop not clear.

Response 20: The unclear phrasing in Line 335 has been revised to: 'As shown in Fig. 8.1, the observed data points fall precisely on the TM-parameter curve, not on the EM-parameter curve.‘, with all modifications highlighted in red.

Point 21: Line 353 delete the stray period.

Response 21: The extraneous period in Line 353 has been deleted .

Point 22: Line 387 shift.

Response 22: The term 'shift' in Line 387 has been modified as suggested, with the revision highlighted in red in the updated manuscript.

Point 23: Line 388 which reduces the complexity.

Response 23: The clause 'which reduces the complexity' in Line 388 has been modified as suggested, with the correction highlighted in red.

Point 24: Line 393 sharp instead of sharply.

Response 24: The adverbial form 'sharply' has been revised to the adjective 'sharp' in Line 393 as grammatically required, with the change highlighted in red.

Point 25: Line 394 efficiently improving.

Response 25: The phrase 'efficiently improving' in Line 394 has been modified as suggested, with the correction highlighted in red in the revised manuscript.

Point 26: Line 396 from increasing AV cannot.

Response 26: The phrase 'from increasing AV cannot' in Line 396 has been modified as recommended, with the correction highlighted in red in the revised manuscript.

Point 27: Line 398 Increasing AV by 20% is smaller than that of the speed.

Response 27: The comparative statement 'Increasing AV by 20% is smaller than that of the speed' in Line 398 has been modified for improved clarity and technical accuracy, with the revision highlighted in red in the updated manuscript.

Response to Reviewer 2 Comments

1. Summary

We sincerely thank you for your detailed and constructive comments. In response, we have carefully revised the manuscript to address all the points you raised. We clarified discrepancies in dataset allocation, unified speed units across text and figures, and made several improvements to the flowcharts in Figures 3 and 7 to enhance logical structure and readability. Parameter explanations in Sections 4.2.4 and 5.2 have been expanded with methodological justifications based on empirical data and safety considerations. Figure references and the order of plots have been corrected, and graphical quality has been improved for better readability. We also clarified the meaning of “Position” in trajectory plots and added explanations regarding the analysis of curvature. Additionally, the Discussion section has been strengthened with quantitative results and further interpretation to support the study's findings. All revisions are clearly highlighted in the revised manuscript. We are grateful for your thoughtful suggestions, which have helped improve the clarity, rigor, and presentation of our work.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

Yes

Are the research design, questions, hypotheses and methods clearly stated?

Can be improved

Are the arguments and discussion of findings coherent, balanced and compelling?

Can be improved

For empirical research, are the results clearly presented?

Can be improved

Is the article adequately referenced?

Yes

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

Can be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Line 137 states that “70% samples are for model validation”. However, Table 3 shows that about 30% of the samples were taken for validation. This should be checked and corrected.

Response 1: The text in Line 137 has been revised to clarify the dataset allocation: 'Seventy percent samples for HDV training. Thirty percent samples are for model validation.' This correction aligns precisely with Table 3 (showing 30% validation samples), with all changes highlighted in red.

Comments 2: In Figure 1, the unit of speed is km∙h^-1, but in the text (Line 153) m·s^-1 is used. This complicates the comparison of the obtained results and the perception of conclusions from them. I recommend bringing it into compliance.

Response 2: All speed units have been unified to km∙h^-1 throughout the manuscript. All modifications are highlighted in red for verification. This ensures consistent interpretation and direct comparison of speed-related data.

Comments 3: In the flowchart (Figure 3) there are two identical “Velocity update” operators that should be combined. The “Rhombus” element of the flowchart should contain a condition check. Now this condition is written to the left of the given figure. I recommend presenting “Monitoring the front conditions” as a block preceding the condition check. This could be, for example, a block-action (a rectangle) or a sub-program (a rectangle with two side lines).

Response 3: We have carefully revised Figure 3 according to the reviewer's suggestions. The updated flowchart now features a consolidated "Velocity update" operator, incorporates the condition check within the rhombus element, and presents "Monitoring the front conditions" as a distinct preceding action block. These modifications have improved the logical flow and clarity of the diagram while maintaining the original algorithm's functionality.

Comments 4: Section 4.2.4 states that “the difference between acceleration and deceleration behaviors are only shown on the assignment of values of kp and kd”. Then what explains that the same values are used for both the acceleration and deceleration modes kp=0.45?

Response 4: We have carefully revised Section 4.2.4 to provide clearer explanation of the parameter assignments for acceleration and deceleration behaviors. The updated text now explicitly specifies that while the same k_p value (0.45) is used for both acceleration and deceleration modes, distinct k_d values are employed to differentiate these behaviors. The modified content has been marked in red in the text.

Comments 5: The algorithm presented in Figure 7 can be optimized by changing the order of repeated operators (blocks) and removing unnecessary ones. For example, it is advisable to combine identical final blocks “t=t+1”. In addition, the reference to this Figure in Line 317 should be changed.

Response 5: The algorithm in Figure 7 has been optimized as suggested, with all modifications highlighted in red. The reference in Line 317 has been updated accordingly.

Comments 6: From the text of Section 5.2, it remains unclear why these threshold values were taken to determine the acceleration/uniformity or deceleration mode. I recommend adding this to this section.

Response 6: The threshold values for distinguishing between acceleration/uniform and deceleration modes in Section 5.2 have been clarified to address the reviewer's concern. Specifically, we have added explicit justification that these thresholds (0.78 s for PAV-P, 1.05 s for PAV-T, 1.57 s for TAV-P, and 1.25 s for TAV-T) correspond to the 25th percentile of observed time headways in the AV car-following dataset. This selection is based on the safety consideration that vehicles tend to decelerate when headways fall below this critical value, indicating reduced safety margins. The revised text now provides this methodological rationale, with all modifications clearly highlighted in red for easy reference.

Comments 7: Lines 334 and 342 may contain incorrect numbers in the reference to the figures. This should be checked.

Response 7: The incorrect figure references in Lines 335 and 343 have been updated to the accurate numbering, with all modifications highlighted in red.

Comments 8: The graphs in Figures 8.2 and 8.3 are reversed. The order of the graphs should be taken as in Figure 8.1. In addition, the quality of these drawings is not sufficient for quick recognition of markers. This should be corrected.

Response 8: Thank you for your observation. The order of the graphs in Figures 8.2 and 8.3 has been revised to match that of Figure 8.1, as suggested. 

Comments 9: What is meant by “Position” in Figures 9.1-9.3 and how is curvature in vehicle trajectories determined accordingly (Section 6.2)?

Response 9: We sincerely appreciate the reviewer's insightful question regarding the interpretation of "Position" in Figures 9.1-9.3 and the curvature analysis. In our study, "Position" specifically refers to the longitudinal distance along the analyzed road segment, representing the spatial dimension of vehicle trajectories. Each trajectory line in these figures illustrates a vehicle's movement pattern, where: The slope of each trajectory line reflects the speed characteristics of the vehicle - the steeper the slope, the faster the speed, while the gentler the slope, the slower the speed. This visualization approach allows for clear comparison of speed profiles across different vehicle types and driving conditions.

Comments 10: In the "Discussion" section, I recommend adding quantitative values of the obtained results, for example, regarding the accuracy of the models, as well as their discussion.

Response 10: We have enhanced the Discussion section by incorporating quantitative results to strengthen our analysis, with all additions highlighted in red for easy reference. 

Response to Reviewer 3 Comments

1. Summary

We sincerely thank you for your detailed and constructive feedback on our manuscript. We deeply appreciate the time and effort you invested in reviewing our work. Your insightful suggestions have greatly contributed to enhancing both the quality and clarity of our study. In response to your comments, we have made several important revisions to address your concerns.

This study primarily investigates the fundamental car-following behaviors of various vehicle types in automated driving environments. We recognize the significance of factors such as communication delays and perception errors and plan to incorporate these aspects in our future research. The simplifications regarding vehicle combinations and the decision to keep the preceding vehicle constant were intentional, aimed at maintaining a focused examination of following vehicle behaviors. As for your suggestion on lane-changing behavior, we agree it is a crucial aspect of traffic flow and will expand our investigation in future work, building upon our current single-lane analysis.

In response to your comments on figure clarity, we have improved the readability of Figure 7 and added color to Figures 8.1-8.3 to enhance visual distinction. Additionally, we have clarified that real-world trajectory data was used for model calibration, ensuring that our simulation accurately reflects actual driving behaviors. While we acknowledge the advancements in vehicle communication and eco-driving techniques, our current focus is on understanding the fundamental interactions between vehicle types, which form the basis for these technologies.

All revisions have been carefully incorporated into the manuscript, and we believe these changes have significantly strengthened our work. We are grateful for your thorough review, which has played a vital role in improving both the technical content and the presentation of our research.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

Yes

Are the research design, questions, hypotheses and methods clearly stated?

Yes

Are the arguments and discussion of findings coherent, balanced and compelling?

Can be improved

For empirical research, are the results clearly presented?

Must be improved

Is the article adequately referenced?

Can be improved

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: The article assumes that AV operates completely according to the CACC model during acceleration and deceleration, ignoring communication delays, perception errors, and control execution deviations in reality. Suggest the author to set error perturbations in the simulation for sensitivity analysis to enhance the robustness of the model.

Response 1: This study focuses on investigating the fundamental differences in car-following behaviors between various vehicle-type combinations (passenger cars and trucks) under automated driving conditions, rather than developing new AV control algorithms or addressing real-world implementation challenges. Although factors such as communication delays, perception errors, and control deviations are crucial in practical AV deployment, they are not considered in the current analysis and will be incorporated in future work to enhance the realism and applicability of the findings.

Comments 2: This article simplifies the vehicle combination into 8 following modes (such as PHDV-P, THDV-T, etc.), but does not consider the impact of whether the preceding vehicle is an AV on the behavior of the following vehicle.

Response 2: This study focuses specifically on investigating the car-following characteristics of different vehicle types in automated driving environments, with particular attention to how following vehicles behave. While we recognize that the type of preceding vehicle (AV vs HDV) could potentially influence following behavior, our current research intentionally maintains a controlled approach by keeping the preceding vehicle characteristics constant. This methodological choice allows us to isolate and clearly identify the fundamental behavioral differences between various following vehicle types without introducing additional complexity from bidirectional interactions. 

Comments 3: This study is based solely on single lane simulation and ignores the significant impact of lane changing behavior on traffic flow stability. Suggest expanding the work to modeling multi lane and lane changing.

Response 3: This study focuses specifically on car-following behavior, not lane-changing, hence the single-lane scenario was selected to isolate vehicle-type interactions. We have clarified this rationale in the revised manuscript and highlighted the relevant text in red Future work will examine how vehicle types affect lane-changing behavior.

Comments 4: The text in each module in Figure 7 is too small to recognize. It is recommended to simplify the figure.

Response 4: We carefully modified Figure 7, which retains the complete method details of our algorithm, while greatly improving visual clarity by increasing font size and making more efficient use of space.

Comments 5: In Figures 8.1, 8.2, and 8.3, the different curves only differ in line shape without color difference. It is recommended to use different colors for different curves.

Response 5: We improved Figures 8.1-8.3 by adding color differences to compare existing different lines.

Comments 6: In the experimental section, in addition to the simulation environment, it is recommended to use real-world datasets for simulation experiments, as real-world datasets can better represent the real behavior of drivers.

Response 6: This study is fundamentally grounded in empirical driving behavior through the use of real-world trajectory data for model calibration and validation. The simulated annealing algorithm was specifically employed to optimize car-following model parameters to best match observed driving patterns across different vehicle-type combinations. The parameter calibration process ensures the simulation results reflect authentic driving characteristics captured in the field data.

Comments 7: The latest technical advancements on vehicle following methods should be reviewed for completeness. For instance, Optical communication based V2V for vehicle platooning; Eco-Driving Framework for Hybrid Electric Vehicles in Multi-Lane Scenarios by Using Deep Reinforcement Learning Methods.

Response 7: This study specifically focuses on fundamental car-following behaviors in mixed automated driving environments, intentionally excluding both lane-changing behavior and full V2V-connected scenarios. While we acknowledge the importance of multi-lane environments and advanced V2V technologies, these aspects fall outside the current scope of investigating basic following behaviors between different vehicle types. The foundational understanding gained from this work will directly support our planned future research incorporating lane-changing behavior analysis and V2V communication environments. 

Response to Reviewer 4 Comments

1. Summary

We sincerely thank you for your thoughtful and detailed comments, which have greatly contributed to improving the clarity and rigor of our manuscript. This study aims to explore the fundamental differences in car-following behaviors across various vehicle-type combinations in automated driving environments. In response to your suggestions, we have clarified the modeling assumptions, including the focus on vehicle type and automation level as the primary determinants, and we have acknowledged the limitations regarding individual and environmental factors. The basis and validity of our classification into eight car-following patterns have been further elaborated through statistical and cluster analysis grounded in empirical data.

We also clarified the rationale behind focusing on single-lane simulations, emphasizing that this simplification enables clearer interpretation of vehicle-type interaction effects, with multi-lane dynamics considered as a direction for future work. Regarding the use of the HighD dataset, we provided justification based on its suitability for capturing vehicle-type-specific behaviors under realistic highway conditions. Additionally, we responded to your concerns about the model's practical applicability, explaining that issues like communication delay and sensor failure are beyond the current study's scope. While no new algorithms were proposed, the value of the study lies in its systematic behavioral comparison across all vehicle-type pairings. Finally, we discussed our choice of the simulated annealing algorithm for parameter calibration in light of its effectiveness in this context.

All relevant clarifications and revisions are included in the manuscript and marked with track changes. We appreciate your insights, which have helped us refine the manuscript's focus and presentation.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

Can be improved

Are the research design, questions, hypotheses and methods clearly stated?

Can be improved

Are the arguments and discussion of findings coherent, balanced and compelling?

Can be improved

For empirical research, are the results clearly presented?

Can be improved

Is the article adequately referenced?

Can be improved

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

Can be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: It is assumed that car-following behavior is only determined by vehicle type and automation level, but the influence of individual differences and environmental factors is ignored. The applicability of the model is doubtful.

Response 1: The current study intentionally focuses on vehicle-type-specific behaviors (P-P, P-T, T-P, T-T combinations) as the primary determinant of car-following patterns, which represents our core research objective. While environmental factors were not considered (as noted by the reviewer), our model does account for individual differences through: Systematic variations between different vehicle-type pairings. Distinct parameter sets for each combination case. Demonstrated behavioral differences in acceleration/deceleration patterns. The vehicle-type-focused approach provides fundamental insights that can serve as a basis for future work incorporating environmental factors.

We acknowledge that expanding the model to include environmental influences would be valuable for operational applications, and we have added this as a recommended direction for future research in the revised manuscript However, the current simplification allows clearer isolation and analysis of vehicle-type effects, which was our primary research goal.

Comments 2: When car-following behavior is classified into 8 patterns, the detailed explanation and validity verification of classification basis are lacking.

Response 2: The classification of car-following behavior into 8 patterns is grounded in “empirical analysis of field data”. Our analysis of HDV car-following characteristics revealed statistically significant differences in speed profiles, acceleration patterns, and headway distributions across the four vehicle-type combinations (P-P, P-T, T-P, T-T). These differences were confirmed through rigorous statistical testing, including Kolmogorov-Smirnov tests, which demonstrated the need for distinct model parameters for each combination. The validity of this classification approach is further supported by cluster analysis of behavioral patterns observed in real-world driving conditions. 

Comments 3: The model is too simplistic, and simulation experiments only consider the single-lane scene, completely ignoring the impact of lane change behavior on mixed traffic flow in multi-lane traffic.

Response 3: This study focuses specifically on car-following behavior, not lane-changing, hence the single-lane scenario was selected to isolate vehicle-type interactions. We have clarified this rationale in the revised manuscript and highlighted the relevant text in red Future work will examine how vehicle types affect lane-changing behavior.

Comments 4: Only the HighD dataset was used for calibration and validation, and cross-validation with other independent datasets was lacking. This made the reliability and generalization ability of the model insufficiently demonstrated.

Response 4: The use of the HighD dataset for model calibration was carefully selected to match our research focus on characterizing vehicle-type-specific interactions in car-following behavior. While we acknowledge that additional datasets could further validate the model's generalizability, the HighD dataset provides several unique advantages for our specific study objectives. Its detailed trajectory data captures the full spectrum of vehicle-type combinations (passenger cars and trucks) under real highway conditions, allowing us to precisely analyze how different vehicle pairings affect following behaviors.

Comments 5: For the modeling of autonomous vehicles, the effects of communication delay and sensor failure on their car-following performance are not fully considered, which makes the conclusion that AV penetration improves traffic capacity lack sufficient practical support.

Response 5: The study focuses primarily on examining how different vehicle-type combinations (particularly between passenger cars and trucks) affect car-following behaviors in automated driving environments, rather than developing new autonomous vehicle control algorithms. While we acknowledge that communication delays and sensor failures represent important practical considerations for AV implementation, these factors fall outside the immediate scope of our current investigation into fundamental vehicle-type interaction patterns.

Comments 6: The improved car-following decision-making framework is a simple parallel connection of traditional IDM and CACC, and no new theory or algorithm is proposed.

Response 6: This study focuses on investigating the behavioral differences between various vehicle-type combinations (passenger cars and trucks) in automated driving environments, rather than proposing new car-following theories. While we employ established IDM and CACC models as our foundation, our key contribution lies in systematically analyzing and comparing the car-following characteristics across all possible vehicle pairings under automated driving conditions.

Comments 7: In the process of parameter calibration, only simulated annealing algorithm is used, but it is not compared with other mainstream optimization algorithms, which reduces the reliability of calibration results.

Response 7: This study focuses primarily on examining the car-following behavior differences between passenger cars and trucks in automated driving environments, rather than conducting comparative analysis of optimization algorithms. The use of simulated annealing for parameter calibration was carefully selected based on its established capability to handle complex, nonlinear optimization problems in transportation research. Our validation process confirmed that this approach reliably produces parameter sets that generate realistic vehicle trajectories consistent with field observations.