Enhancing Sustainable Mobility: A Comparative Analysis of C-ITS and Fundamental Diagram-Based Traffic Jam Detection

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. Some references should be added to support the writing of the Introduction. Especially important statements regarding C-ITS services and FD.
2. The sources of some parameters must be substantiated, such as the parameters of lane-changing model.
3. In Section 2.2, the authors directly adopted Newell's three-parameter macroscopic model. It is recommended that the authors briefly explain the reasons for choosing the model.
4. Please check whether the data ‘2.33’ in Table 2 is correct.
5. The authors' argument is somewhat confusing. This manuscript focuses throughout on comparing C-ITS services and FD, but the final conclusion recommends combining the two methods.
6. The title of Figure 6 only mentions (a) and (b), but there are four sub-figures in the figure.。
7. The answers to the two key questions raised by authors are not very clear. The conclusion does not clearly indicate the advantages and disadvantages of both methods.
8. It is recommended that a short paragraph be added to the conclusion section specifically discussing the practical implications of this study for traffic management departments or policy makers.

Author Response

Dear Reviewer, We would like to extend our sincere gratitude for your time, effort, and insightful feedback on our manuscript. Your constructive comments helped us to strengthen the paper's clarity, rigour, and overall contribution. We have carefully considered all the suggestions and have thoroughly revised the manuscript to address the points raised.
To facilitate your review of the updated version, all changes, additions, and modifications within the text have been marked in red.

Comments 1: [Some references should be added to support the writing of the Introduction. Especially important statements regarding C-ITS services and FD.]
Response 1: [We thank the reviewer for this valuable suggestion. We fully agree that it is essential to support the foundational statements in the introduction with specific and relevant literature. In response to this comment, we have carefully revised the entire section and integrated new citations to substantiate our key claims. Specifically, we have added references to support: the general definition and objectives of C-ITS services; the role of the Fundamental Diagram (FD) as a cornerstone of traffic flow theory and its application in Traffic State Estimation (TSE); statements regarding the practical limitations of fixed sensors, such as costs and spatial coverage gaps; the deployment challenges of C-ITS, including the dependency on market penetration and communication reliability.]

Comments 2: [The sources of some parameters must be substantiated, such as the parameters of the lane-changing model.]
Response 2: [We agree on the importance of explicitly indicating the source of these parameters. We have now clarified in Section 4.1 that the microscopic parameters used to characterize driver behaviour—including those for the lane-changing model (lcAssertive, lcSpeedGain, lcKeepRight)—are based on the calibrated and validated values reported in the work of [Berrazouane, M., Tong, K., Solmaz, S., Kiers, M., \& Erhart, J. (2019, November). Analysis and initial observations on varying penetration rates of automated vehicles in mixed traffic flow utilizing sumo. In 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) (pp. 1-7). IEEE.].]

Comments 3: [In Section 2.2, the authors directly adopted Newell's three-parameter macroscopic model. It is recommended that the authors briefly explain the reasons for choosing the model.]
Response 3: [In response to this observation, we have clarified our choice in section 2.2. We explain that the Newell model, despite its simplicity, was selected for its well-documented empirical validity in representing queue discharge and congested traffic conditions. Its computational efficiency—particularly advantageous for running many stochastic simulations—and its direct, interpretable linkage between microscopic driver behaviour and macroscopic traffic flow dynamics. We believe these features make it a suitable choice for a comparative study focused on the timeliness and localization of initial congestion detection.]

Comments 4: [Please check whether the data ‘2.33’ in Table 2 is correct.]
Response 4: [We are grateful to the reviewer for their careful reading and for identifying this error. The reviewer is correct; the data point ‘2.33’ in the original Table 2 was erroneous. Upon re-examining our simulation logs, we found not only a typographical error, but also that the values for 'Hour 1' and 'Hour 2' for the 'Sec-1-5min' test case had been inadvertently swapped. The value '2.33' should have been '23.3%', representing the detection percentage in 'Hour 2'. The correct detection percentage for 'Hour 1' in that scenario is 100%.
We have now corrected the table in the revised manuscript to show '100%' under 'Hour 1' and '23.3%' under 'Hour 2' for the 'Sec-1-5min' case. ]

Comments 5: [The authors' argument is somewhat confusing. This manuscript focuses throughout on comparing C-ITS services and FD, but the final conclusion recommends combining the two methods.]
Response 5: [We are grateful to the reviewer for identifying this crucial point on the manuscript's narrative structure. The reviewer's insight prompted us to undertake a significant restructuring of the paper's final sections. By introducing a dedicated Discussion section (Section 6), we now establish a clear and robust logical bridge between our comparative findings and our ultimate recommendation for a hybrid approach. This new structure, supported by the systematic comparison in the new Table 3, not only resolves the initial ambiguity but, we believe, has substantially strengthened the paper's overall argument and contribution.]

Comments 6: [The title of Figure 6 only mentions (a) and (b), but there are four sub-figures in the figure.]
Response 6: [We thank the reviewer for this correction. The caption of Figure 6 was indeed incomplete. We have revised it to accurately describe all four subplots: (a) Detectors with 1-minute aggregation, (b) Detectors with 3-minute aggregation, (c) Detectors with 5-minute aggregation, and (d) C-ITS service with varying penetration rates.]

Comments 7: [The answers to the two key questions raised by authors are not very clear. The conclusion does not clearly indicate the advantages and disadvantages of both methods.]
Response 7: [We thank the reviewer for this important feedback. We agree that the clarity of our findings in the final sections of the manuscript could be significantly improved. To address this, we have undertaken a substantial revision of both the Discussion (Section 6) and Conclusion (Section 7) sections, while keeping them distinct. In the revised Discussion section (Section 6), we now explicitly structure the text to provide direct answers to the two research questions posed in the Introduction. We have sharpened the language to clearly articulate the performance of each method. Furthermore, we have revised the text that introduces Table 3 to explicitly frame it as a summary of the distinct advantages and disadvantages of the FD-based and C-ITS-based approaches across several key operational dimensions. The Conclusion section (Section 7) has been completely rewritten to be more focused and impactful. It no longer repeats the detailed results, but instead synthesizes the main takeaways from the discussion. It now provides a concise, high-level summary of the core findings and reinforces the practical implications derived from the comparative analysis.]

Comments 8: [It is recommended that a short paragraph be added to the conclusion section specifically discussing the practical implications of this study for traffic management departments or policy makers.]
Response 8: [We thank the reviewer for this suggestion to enhance the impact of our work. We agree that explicitly discussing the practical implications is essential. To address this, we have added a new, dedicated subsection to the Conclusion titled "Practical Implications for Traffic Management and Policy". This new section discusses how our results can inform strategic decisions on infrastructure investment, such as prioritizing the deployment of C-ITS infrastructure (e.g., Roadside Units) at known bottleneck locations over simply place along the network an even larger number of traditional loop detectors. Furthermore, it outlines how traffic management centres can begin to integrate C-ITS data as a valuable supplementary source to improve situational awareness and enable more proactive traffic control strategies, even at the current low penetration rates. 

]

Reviewer 2 Report

Comments and Suggestions for Authors

1. This paper conducts a comparative study on two methods for traffic congestion detection: one based on the traditional Fundamental Diagram (FD) and the other based on the services of Cooperative Intelligent Transportation Systems (C-ITS). Through simulation experiments, the performance of the two methods under different traffic conditions is evaluated, and their advantages and disadvantages as well as potential integration schemes are discussed. The topic of the paper is of practical significance, and the research design is relatively rigorous, but it still needs further improvement in some aspects.
2. The introduction section can further emphasize the practical challenges of current traffic congestion detection technologies (such as the limitations of fixed sensors or the obstacles in the promotion of C-ITS deployment), in order to highlight the necessity of this study. Although relevant literature has been cited, the existing research on the FD model and C-ITS services can be more systematically compared. For instance, the performance differences between the two methods in the existing studies can be summarized or integrated attempts can be made.
3. TRCO_3 and TRCO_5 were not implemented in the simulation. It is necessary to explain whether this will affect the generalizability of the results.
4. It is possible to discuss the impact of other potential triggering conditions (such as weather or accidents) on the service performance.
5. The detection of C-ITS services depends on the distribution of vehicles, while the FD method is limited by the position of the detectors. It is possible to further discuss how to enhance performance by optimizing the layout of detectors or the communication range of vehicles.
6. The current simulation scenario involves a single lane with a bottleneck. It is necessary to clarify whether the results are applicable to more complex road networks (such as intersections or multi-bottleneck scenarios).
7. Some abbreviations (such as CCV) were not defined when they first appeared, and their full names need to be provided.

Author Response

Dear Reviewer, We would like to extend our sincere gratitude for your time, effort, and insightful feedback on our manuscript. Your constructive comments helped us to strengthen the paper's clarity, rigour, and overall contribution. We have carefully considered all the suggestions and have thoroughly revised the manuscript to address the points raised.
To facilitate your review of the updated version, all changes, additions, and modifications within the text have been marked in red.

Comments 1: [This paper conducts a comparative study on two methods for traffic congestion detection: one based on the traditional Fundamental Diagram (FD) and the other based on the services of Cooperative Intelligent Transportation Systems (C-ITS). Through simulation experiments, the performance of the two methods under different traffic conditions is evaluated, and their advantages and disadvantages as well as potential integration schemes are discussed. The topic of the paper is of practical significance, and the research design is relatively rigorous, but it still needs further improvement in some aspects.]
Response 1: [We would like to express our sincere gratitude to the reviewer for their positive assessment of our work's practical significance and rigorous research design. We also appreciate the constructive feedback and agree that several aspects of the manuscript could be strengthened to improve its overall quality and impact. We have undertaken a comprehensive revision of the manuscript, and the main changes include:

• We have significantly expanded the Introduction to better contextualize the study. This includes a more in-depth discussion of the practical challenges of current detection technologies, a more systematic comparison of existing research on FD and C-ITS, and the inclusion of numerous recent references. This ensures the paper is grounded in the current state-of-the-art.
• We have added explicit justifications for our key methodological choices, including a new subsection detailing the rationale for selecting Newell's model. We have also clarified the sources for all simulation parameters and discussed the implications of not implementing certain C-ITS service conditions, framing our results as a conservative estimate of C-ITS performance.
• The final sections of the paper have been completely restructured to be more direct and impactful. We now explicitly answer the two key research questions posed in the introduction, clearly outline the advantages and disadvantages of each method (supported by a new summary table). We also include a new subsection dedicated to the practical implications of our findings for policymakers and traffic management authorities.
• We have carefully addressed all specific points regarding figures, tables, and abbreviations throughout the manuscript to ensure accuracy and clarity.]

Comments 2: [The introduction section can further emphasize the practical challenges of current traffic congestion detection technologies (such as the limitations of fixed sensors or the obstacles in the promotion of C-ITS deployment), in order to highlight the necessity of this study.]
Response 2: [We agree that a stronger emphasis on the practical challenges of existing technologies makes the need for a direct comparative analysis, such as the one we have conducted, more apparent. To address this point, we have undertaken a substantial rewrite of the Introduction section. We have introduced new paragraphs dedicated to detailing the real-world challenges associated with each paradigm:

• We now explicitly discuss the problems of "spatial gaps" in network coverage, which prevent the capture of full spatio-temporal congestion dynamics, and the high capital and maintenance costs that make widespread installation impractical.
• We now highlight the "network effect" dilemma related to the need for a critical mass of equipped vehicles to ensure service effectiveness, as well as the technical challenges of wireless communication, such as latency, data packet loss, and cybersecurity vulnerabilities.

This new framing contextualizes our study not as a simple comparison of two technologies, but as a direct response to well-known, practical issues in the field. In doing so, the necessity of our research emerges more clearly and compellingly, justifying the importance of understanding the trade-offs between an established but limited approach and an emerging but not yet fully mature one.]

Comments 3: [Although relevant literature has been cited, the existing research on the FD model and C-ITS services can be more systematically compared. For instance, the performance differences between the two methods in the existing studies can be summarized or integrated attempts can be made]
Response 3: [We are very grateful to the reviewer for this insightful recommendation. We agree completely that a more systematic, literature-based comparison of the two paradigms, coupled with a discussion of their potential for integration, significantly deepens the paper analytical contribution. This suggestion has provided a clear roadmap for improving the Discussion section.
To address this comment comprehensively, we have some major additions to the manuscript. More specifically, we have added a new Discussion section that synthesizes the performance characteristics of FD-based and C-ITS-based methods, drawing from the broader literature to contextualize the findings from our specific simulation study. The section contain a new Table 3 that systematically compares the two methods across a range of key performance and deployment metrics. These include the detection principle, spatial and temporal resolution, data granularity, robustness, deployment cost, and, importantly, their potential for integration.]

Comments 4: [TRCO-3 and TRCO-5 were not implemented in the simulation. It is necessary to explain whether this will affect the generalizability of the results.]
Response 4: [This is an important point that merits clarification, and we thank the reviewer for raising it. We have modified the section 2.3 to explicitly discuss this limitation and its impact on the generalizability of our findings.
In our response, we explain that the non-implementation of TRCO-3 (notifications via mobile radio) and TRCO-5 (detection via on-board sensors) makes our simulation a conservative case study for C-ITS performance. In our scenario, congestion detection is entirely dependent on a CCV receiving CAM or DENM messages from other nearby connected vehicles. In a real-world context, a vehicle could detect a traffic jam through its own on-board sensors (TRCO-5) or receive a warning via the cellular network (TRCO-3), even in the absence of other CCVs in its immediate vicinity. By omitting these alternative data channels, our simulation places a higher burden on direct V2V/V2I communication.
Therefore, we argue that our results likely represent a lower bound on the real-world performance of the Traffic Jam Ahead service. The actual performance is expected to be better, as vehicles would have more redundant and independent ways to detect congestion. Furthermore, we have added a subsection "Limitations and Future Directions" within the Conclusion. Here, we explicitly state the partial implementation of the C-ITS service as a limitation and identify the inclusion of all trigger conditions (TRCO-3 and TRCO-5) as a primary direction for our future research. This allows for a more comprehensive evaluation, especially in scenarios with low penetration rates where these alternative data channels would be most impactful.]

Comments 5: [It is possible to discuss the impact of other potential triggering conditions (such as weather or accidents) on the service performance.]
Response 5: [The reviewer correctly points out that events like accidents and adverse weather are critical factors affecting traffic. Within the standardized C-ITS framework (e.g., ETSI EN 302 637-2), such non-recurring events are typically handled by other, dedicated services, rather than the Traffic Jam Ahead service, which is primarily designed for congestion arising from high traffic demand (i.e., recurring congestion). For instance:

• An accident or a stationary vehicle would trigger a "Hazardous Location Warning" or "Stationary Vehicle Warning" DENM.
• Adverse weather conditions (e.g., heavy fog, ice) would trigger an "Adverse Weather Condition Warning" DENM.

While the consequence of these events—namely, the formation of a queue—would eventually be detected by the Traffic Jam Ahead service we studied, the initial event detection and warning are handled by a different, more appropriate service within the C-ITS suite.  A full simulation of these complex, stochastic event chains and the interaction between different C-ITS services is a substantial undertaking that falls outside the scope of our current paper. This paper is focused on a direct performance comparison for recurring, bottleneck-induced congestion. To address the reviewer's point and clarify this for the reader, we have added a brief discussion of this topic in the newly expanded "Limitations and Future Directions" subsection of the Conclusion section of the revised manuscript. We acknowledge that analyzing the system's performance under such non-stationary conditions is a critical area for future research.]

Comments 6: [The detection of C-ITS services depends on the distribution of vehicles, while the FD method is limited by the position of the detectors. It is possible to further discuss how to enhance performance by optimizing the layout of detectors or the communication range of vehicles.]
Response 6: [We agree that a more explicit discussion on how our findings inform optimization strategies would significantly enhance the paper. Our results, particularly from the second-hour simulation where the less severe congestion was missed by detectors 2 and 3, clearly demonstrate the rigid spatial dependency of the FD method. Conversely, the C-ITS approach showed inherent spatial flexibility, limited only by the presence of a connected vehicle. To address the reviewer's suggestion, we have made two key additions to the manuscript:

• We have added a new paragraph at the end of the Results section. This paragraph  discusses the fundamental trade-off between the FD method's static, position-based limitation and the C-ITS method's dynamic, penetration-based dependency, using the simulation results as direct evidence.
• We have expanded the "Practical Implications for Traffic Management and Policy" subsection within the Conclusion. In this section, we argue that our findings suggest a clear optimization strategy: rather than investing in a denser layout of costly fixed detectors, a more effective approach for known bottlenecks would be the strategic deployment of C-ITS infrastructure, such as Roadside Units (RSUs). RSUs can ensure reliable communication and data collection precisely where it is needed most, effectively leveraging the spatial flexibility of the C-ITS paradigm.]

Comments 7: [The current simulation scenario involves a single lane with a bottleneck. It is necessary to clarify whether the results are applicable to more complex road networks (such as intersections or multi-bottleneck scenarios).]
Response 7: [We thank the reviewer for this crucial question regarding the generalizability of our findings. We agree that it is essential to clarify the applicability of our results to different network contexts.
Our choice of a straight, multi-lane road with a single lane-drop bottleneck was a deliberate methodological decision. This setup, which is a standard paradigm in traffic simulation literature, allowed us to create a controlled environment for a direct and rigorous comparison between the FD-based and C-ITS-based detection methods. As noted by Grumert et al. [Grumert, E. F., Tapani, A., \& Ma, X. (2018). Characteristics of variable speed limit systems. \textit{European transport research review}, \textit{10}(2), 21. ], this type of scenario is well-established for emulating the formation of recurring, non-incident-related congestion on freeways. By simplifying the network topology, we could isolate and clearly analyze the core performance trade-offs: the spatial dependency of the FD method versus the penetration rate dependency of the C-ITS method. 
Regarding applicability, we argue that our findings are highly generalizable to other extra-urban and freeway environments. The fundamental principles we observed—that fixed sensors are limited by their physical location while connected vehicles offer spatial flexibility at the cost of requiring a critical mass—would remain valid in more complex freeway networks with multiple bottlenecks or interchanges. The interaction between these factors might become more complex, but the underlying trade-off persists.
Conversely, we explicitly state that our results are not directly applicable to complex urban networks. The traffic dynamics in urban environments, characterized by intersections, signalized control, frequent stop-and-go waves, and interactions with vulnerable road users, are fundamentally different. Crucially, the core assumptions of macroscopic models like the Fundamental Diagram often do not hold in such non-homogeneous, interrupted-flow conditions, which would invalidate the basis of our comparison.
To make this distinction clear to the reader, we have added a detailed paragraph to the "Limitations and Future Directions" in the Conclusion section of the revised manuscript. This new text explicitly discusses the generalizability of our findings to freeway contexts and clarifies why they should not be extrapolated to urban scenarios, while also positioning the analysis of more complex networks as a key direction for future research.]

Comments 8: [Some abbreviations (such as CCV) were not defined when they first appeared, and their full names need to be provided]
Response 8: [Thank you for noticing this omission. We have corrected the manuscript to define the abbreviation "CCV" (Cooperative and Connected Vehicle) when it first appeared in the text, which is now in the Introduction section. Furthermore, we have conducted a thorough review of the entire manuscript to ensure that all other abbreviations are properly defined at their first appearance, in order to improve the overall clarity and readability of the paper.]

Reviewer 3 Report

Comments and Suggestions for Authors

Figure 1. Fundamental diagram of traffic flow can be represented a little larger, to be more easily visible.
The paper is based on a selective bibliography, not very extensive. Most of the references are older than 5 years. These are relevant to the study, but the paper should also be based on current studies, on similar research related to solutions to reduce traffic jams.

The case study presents a common situation, of switching from more to fewer lanes for a road sector. The introductory part should present real situations from the infrastructure (switching from 3 to 2 lanes in the direction of traffic). Including in the Metropolitan Area where I live, there are many cases of this kind, 2 of them causing very large traffic jams during peak hours. Resident users know about these cases and yet do not use alternative routes configured by navigation applications. Users who do not come from the region use applications and are redirected to the routes with the shortest travel time....

The conclusions summarize what was achieved through this study by the authors.... The conclusions should also include the following:
- Arguments regarding the benefit brought by the experimental method used.
- How does the proposed algorithm differ from other existing methods for predicting traffic jams?
- How can the solution specifically help reduce traffic jams in some specific existing cases (those that should also be presented in the introduction).
- What are the advantages of the proposed solution?
- How will the presented study continue and what will be done in the future in the direction of the authors' research?

Author Response

Dear Reviewer, We would like to extend our sincere gratitude for your time, effort, and insightful feedback on our manuscript. Your constructive comments helped us to strengthen the paper's clarity, rigour, and overall contribution. We have carefully considered all the suggestions and have thoroughly revised the manuscript to address the points raised.
To facilitate your review of the updated version, all changes, additions, and modifications within the text have been marked in red.

Comments 1: [Figure 1. Fundamental diagram of traffic flow can be represented a little larger, to be more easily visible.]
Response 1: [We thank the reviewer for the helpful suggestion. We have increased the size of Figure 1 in the revised manuscript to improve readability and visual clarity.]

Comments 2: [The paper is based on a selective bibliography, not very extensive. Most of the references are older than 5 years. These are relevant to the study, but the paper should also be based on current studies, on similar research related to solutions to reduce traffic jams.]
Response 2: [We thank the reviewer for this valuable suggestion to strengthen the manuscript by incorporating a broader and more current body of literature. We acknowledge that while our original submission cited foundational works on the Fundamental Diagram and C-ITS standards, alongside several recent papers. The paper's context and relevance are significantly enhanced by situating our analysis within the very latest research on advanced traffic management solutions.
In response to this feedback, we have undertaken a comprehensive update of our literature review and have integrated this new context throughout the manuscript, particularly in the Introduction and Discussion sections. The revisions now position our comparative analysis of detection methods as a foundational element within the wider landscape of modern traffic prediction and mitigation strategies.
Specifically, we have incorporated a discussion of the rapid evolution of the field towards proactive traffic state prediction, a domain largely driven by recent advancements in Artificial Intelligence (AI) and Machine Learning (ML). These data-driven models, which aim to forecast and alleviate congestion before it fully materializes, are fundamentally dependent on the quality, resolution, and timeliness of their input data. The two paradigms we investigate—the infrastructure-based FD approach and the vehicle-centric C-ITS approach—represent the primary data-gathering mechanisms that enable these sophisticated predictive systems.
By framing our study in this manner, we now make a clearer and more compelling case for its contribution. A rigorous comparison of the underlying data-gathering mechanisms is not merely an academic exercise; it is a critical prerequisite for the successful development and deployment of the next generation of intelligent transportation systems. Our work provides this foundational analysis, evaluating the raw detection capabilities that serve as the bedrock for these more advanced predictive solutions.
To reflect these enhancements, we have added recent references, with a strong focus on literature published between 2023 and 2025. This ensures that our bibliography is now both extensive and current, directly addressing the reviewer's concern and better reflecting the state-of-the-art in traffic management.]

Comments 3: [The case study presents a common situation, of switching from more to fewer lanes for a road sector. The introductory part should present real situations from the infrastructure (switching from 3 to 2 lanes in the direction of traffic). Including in the Metropolitan Area where I live, there are many cases of this kind, 2 of them causing very large traffic jams during peak hours. Resident users know about these cases and yet do not use alternative routes configured by navigation applications. Users who do not come from the region use applications and are redirected to the routes with the shortest travel time....]
Response 3: [We thank the reviewer for this suggestion. We agree that providing concrete examples of the bottleneck scenario under investigation significantly strengthens the paper's motivation.
To address this, we have added a new paragraph in the Introduction section. This new text now explicitly describes common, real-world examples of infrastructure bottlenecks caused by lane reductions. Citing authoritative sources from the Federal Highway Administration (FHWA) , we mention prevalent scenarios such as forced merges on highways, approaches to bridges or tunnels, and the termination of auxiliary lanes used during peak hours.
This addition serves to demonstrate that our simulation scenario, although simplified for controlled analysis, represents a fundamental and widespread cause of the recurring congestion that traffic authorities face daily.]

Comments 4: [The conclusions summarize what was achieved through this study by the authors.... The conclusions should also include the following:
- Arguments regarding the benefit brought by the experimental method used.
- How does the proposed algorithm differ from other existing methods for predicting traffic jams?
- How can the solution specifically help reduce traffic jams in some specific existing cases (those that should also be presented in the introduction).
- What are the advantages of the proposed solution?
- How will the presented study continue and what will be done in the future in the direction of the authors' research?]
Response 4: [We agree completely that the original conclusion was too brief and did not adequately address the broader contributions and future trajectory of our work.
To address this, we have undertaken a complete restructuring and significant expansion of the Conclusion section so to address each of the points raised:

• The new version of the Conclusion section highlights the benefit of our experimental approach. We explain that using a stochastic co-simulation framework allowed us to conduct a controlled, repeatable, and statistically robust comparison of the two detection paradigms under identical conditions, which would be impossible to achieve in a real-world field test. Moreover, we clarify that our study conducts a comparative performance evaluation of a standardized C-ITS service against a traditional FD method. We then explicitly detail the advantages of the C-ITS approach that emerged from our study—namely its superior spatial flexibility and reliability, especially for less severe congestion. These points are further summarized in the new Table 3 in the Discussion section.
• We have added a new paragraph in the Introduction that describes common, real-world bottleneck scenarios, such as lane drops at highway merges or approaches to tunnels, citing authoritative sources. We then directly connect our findings to these scenarios in the new subsection "Practical Implications for Traffic Management and Policy", explaining how C-ITS deployment could be strategically targeted at these known problem areas.
• Future research directions: We have significantly expanded the final subsection "Limitations and Future Directions", to provide a much more detailed and ambitious research roadmap. This now includes our plans to extend the analysis to complex networks, implement the full suite of C-ITS service triggers, compare our results against advanced AI-based models. Ultimately, we want to develop a novel hybrid detection algorithm that actively fuses data from both sources.]

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

My comments have all been adequately addressed. I think that the revised paper is nicely improved and much easier to follow.

Author Response

Comments 1: My comments have all been adequately addressed. I think that the revised paper is nicely improved and much easier to follow.

Response 1: Dear Reviewer, We are very pleased to hear that you found your comments to have been adequately addressed and that you feel the manuscript has improved. We would like to thank you for your time and constructive input.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have responded carefully and in detail to the reviewer's comments. The reviewer believes that the revised manuscript meets the publication requirements of the journal

Author Response

Comments 1: The authors have responded carefully and in detail to the reviewer's comments. The reviewer believes that the revised manuscript meets the publication requirements of the journal.

Response 1: Dear Reviewer, We are very pleased to hear that you found your comments to have been adequately addressed and that you feel the manuscript has improved. We would like to thank you for your time and constructive input.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors completed the work with most of the suggestions given in the previous review. They mainly completed the bibliography with new and more representative sources.
However, they did not introduce information related to examples and real situations created by traffic jams, since the study is considered to be a purely theoretical one.
In the conclusions, a sentence related to the importance of the study in reducing the time lost by users in traffic jams caused by the reduction in the number of traffic lanes should be introduced.

Author Response

Comments 1: The authors completed the work with most of the suggestions given in the previous review. They mainly completed the bibliography with new and more representative sources. However, they did not introduce information related to examples and real situations created by traffic jams, since the study is considered to be a purely theoretical one. In the conclusions, a sentence related to the importance of the study in reducing the time lost by users in traffic jams caused by the reduction in the number of traffic lanes should be introduced.

Response 1: We thank the reviewer for the positive assessment, for the further guidance on strengthening the manuscript. We agree that grounding our theoretical analysis in the tangible, real-world consequences of traffic congestion significantly strengthened the manuscript's motivation and impact. To address this, we have substantially revised the Introduction and Conclusion sections to bridge the gap between our theoretical framework and its practical application. The variation to the manuscript are highlighted in blue. Specifically:

• We have enriched the opening paragraphs of the Introduction with specific, recent data that quantifies the severe economic, environmental, and social costs of traffic congestion. This new text cites authoritative sources such as the INRIX Global Traffic Scorecard, the European Environment Agency (EEA), and the U.S. Federal Highway Administration (FHWA) to provide concrete examples of lost productivity, wasted fuel, harmful emissions, and adverse public health outcomes.
• To directly link our simulation scenario to well-documented, real-world phenomena, we have added a sentence that explicitly identifies the “lane-drop” setup as a primary example of a “physical bottleneck” in the Introduction. This terminology, frames our scenario not as an abstract exercise, but as a controlled representation of a leading cause of recurring congestion on highway networks.
• We have added some sentences to the Conclusion section. The aim is to highlights that the superior detection capabilities demonstrated in our study are crucial for deploying proactive traffic management strategies aimed at reducing the significant time lost by road users at common bottlenecks. These include those caused by lane reductions.