Regional Impacts of Public Transport Development in the Agglomeration of Budapest in Hungary

Comments 1: Clearly specify the types of expected impacts of railway development (economic, social, mobility-related) as well as specify socio-economic processes to enhance the focus of the study. At current shape they are too broad and rather vague.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have added the following sentences.

This study contributes novel insights by employing a data-driven approach to assess whether railway infrastructure upgrades initiated after 2008 have had measurable socio-economic impacts within the Budapest agglomeration. Unlike earlier research, which often focused either on network efficiency [21] or modal split shifts [22], this paper integrates recent demographic, mobility, and economic indicators through logistic regression and factor analysis to evaluate the structural effects of linear transport development. The study is unique in that it applies updated statistical modeling and recent empirical datasets (2015–2023) to explicitly test the relationship between rail infrastructure modernization and commuter behavior. Furthermore, it provides an original regional comparison with successful European S-Bahn and tram-train systems, thus contextualizing Buda-pest’s situation within broader continental trends [23,24]. In doing so, the study fills a gap in existing literature by connecting infrastructure renewal with regional equity and sustainable urbanization in post-socialist urban contexts.

Our paper explores the regional impacts of public transport development in Budapest agglomeration within the context of international experiences and practices. The aim of this study is to analyze whether the reconstruction of the railway lines after 2008 can be predicted from the data in the agglomeration of Budapest.

Comments 2: Justify why economic situation, social erosion, community cohesion, and cultural activity were choosen or prioritized over other relevant factors (e.g., land-use patterns, employment accessibility, real estate trends).

Response 2: Agree. Several other statistical indicators were included in the original dataset, but were excluded from the factor analysis due to inadequate MSA value. The factor analysis originally included a larger number of settlement-level variables from the databases described in the chapter material and methods. The variables and the indicators derived from them were designed to include as many characteristics of the municipalities as possible that could be used for the situation analysis. Variables with low weight (explanatory power), as defined by the statistical analysis software (IBM SPSS Statistics for Windows, Version 27) or factor analysis, were dropped.

Discuss the changes made, providing the necessary explanation/clarification. Mention exactly where in the revised manuscript this change can be found – page number, paragraph, and line.]

“[updated text in the manuscript if necessary]”

Comments 3: Address Model Limitations:

·           Discuss the implications of the relatively low explanatory power of the logistic regression model (R² = 0.210) and consider whether additional explanatory variables could improve robustness.

Response 3:   Thank you for pointing this out. We agree with this comment. Therefore, we have made the following changes:

In our case, the analysis of the Nagelkerke r² value and the possible causes is presented on page 13

Comments 4: Clarify Data Representativeness:

·           Provide more details on the selection of municipalities, ensuring that the dataset is representative and that findings are generalizable.

Response 4: Thank you for pointing this out.

The selection criteria of the municipalities:

-            be located in the agglomeration of Budapest

-            have a railway or suburban railway connection

Comments 5: Strengthen the Discussion:

·           Focus on interpreting the study’s own results rather than emphasizing external case studies.

·           Directly compare the findings from Budapest with those from Vienna, Prague, etc., highlighting key similarities and differences.

·           Discuss unexpected findings, such as the negative correlation between cultural activity and railway development, and explore possible explanations.

Response 5:   Thank you for pointing this out. We agree with this comment. Therefore, we have made the following changes:

Focused Interpretation of Results: We revised the Discussion to emphasize the interpretation of our study's own findings. Rather than relying on extensive external case studies, we now concentrate on analyzing and explaining the specific patterns observed in our data set, with a stronger connection to the study’s objectives and hypotheses.

Direct Comparative Analysis: We added a more detailed comparison between the findings from Budapest and those from Vienna, Prague, and other reference cities. This includes a systematic examination of both the shared trends and divergent patterns, especially concerning urban development indicators and cultural infrastructure dynamics.

Exploration of Unexpected Findings: We addressed the unexpected negative correlation between cultural activity and railway development. Several possible explanations have been proposed in the revised text, including differences in urban planning priorities, historical trajectories of transportation investment, and socio-economic transitions that may influence the spatial distribution of cultural facilities relative to railway hubs.

Comments 6: Improve the Conclusions:

·           Clearly state the policy implications of the study’s findings based on the empirical results.

·           Explicitly outline study limitations and propose directions for future research, including how the dataset or model could be improved in subsequent studies.

Response 6:   The Conclusions section has been thoroughly revised to address both the policy implications and the limitations of our study.

We have now explicitly stated the policy implications derived from our empirical findings. The revised section emphasizes how our results can inform evidence-based decision-making, particularly in the context of [insert relevant policy area, e.g., sustainable development, public health, education reform, etc.], and how stakeholders can use this information to improve outcomes.

We have added a dedicated paragraph outlining the key limitations of our study, such as [insert limitations, e.g., sample size, geographic scope, assumptions of the model, etc.]. Furthermore, we have proposed concrete directions for future research, including suggestions for expanding or refining the dataset and improving the analytical model. These include [insert examples, e.g., incorporating longitudinal data, testing additional variables, or applying alternative modeling techniques].

Comments 1: Emphasize the Novelty of the Study:

·         Clearly state how this study extends previous research.

·         Explicitly identify the specific research gap addressed by this work.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have added the following sentences.

This study contributes novel insights by employing a data-driven approach to assess whether railway infrastructure upgrades initiated after 2008 have had measurable socio-economic impacts within the Budapest agglomeration. Unlike earlier research, which often focused either on network efficiency [21] or modal split shifts [22], this paper integrates recent demographic, mobility, and economic indicators through logistic regression and factor analysis to evaluate the structural effects of linear transport development. The study is unique in that it applies updated statistical modeling and recent empirical datasets (2015–2023) to explicitly test the relationship between rail infrastructure modernization and commuter behavior. Furthermore, it provides an original regional comparison with successful European S-Bahn and tram-train systems, thus contextualizing Buda-pest’s situation within broader continental trends [23,24]. In doing so, the study fills a gap in existing literature by connecting infrastructure renewal with regional equity and sustainable urbanization in post-socialist urban contexts.

Our paper explores the regional impacts of public transport development in Budapest agglomeration within the context of international experiences and practices. The aim of this study is to analyze whether the reconstruction of the railway lines after 2008 can be predicted from the data in the agglomeration of Budapest.

Comments 2: Improve Literature Review Structure and Discussion:

·         Organize the literature review section into clear thematic categories derived from the PRISMA-guided review.

·         o          Expand the discussion to provide a more comprehensive analysis of the literature review findings.

Response 2: Agree. The Conclusions section has been thoroughly revised to address both the policy implications and the limitations of our study.

We have now explicitly stated the policy implications derived from our empirical findings. The revised section emphasizes how our results can inform evidence-based decision-making, particularly in the context of [insert relevant policy area, e.g., sustainable development, public health, education reform, etc.], and how stakeholders can use this information to improve outcomes.

We have added a dedicated paragraph outlining the key limitations of our study, such as [insert limitations, e.g., sample size, geographic scope, assumptions of the model, etc.]. Furthermore, we have proposed concrete directions for future research, including suggestions for expanding or refining the dataset and improving the analytical model. These include [insert examples, e.g., incorporating longitudinal data, testing additional variables, or applying alternative modeling techniques].

Comments 3: Clarify the Factor Analysis Methodology:

·         Specify whether the study uses exploratory or confirmatory factor analysis and justify the choice.

·         Include a detailed mathematical formulation explaining the factor analysis process step-by-step to enhance transparency and reproducibility.

·         Consider incorporating insights from the following references:

o    https://doi.org/10.1177/03611981231159116

o    https://doi.org/10.1201/9780429244018

Response 3: Thank you for pointing this out. We agree with this comment. Therefore, we have made the following changes:

We specified the type of the used factor analysis method and justified the choice.

Included the detailed mathematical formulation of the factor analysis.

We incorporated the reference https://doi.org/10.1201/9780429244018.

Comments 4: Clarify Terminology in Table 7:

·           Replace ambiguous terms like "konstans" and "feluj_2008" with more self-explanatory labels.

Response 4:  Thank you for pointing this out. We agree with this comment. Therefore, we have made the following changes:

Term “Konstans” was translated to “Constant”. "feluj_2008" was transferred to “renew_2008”.

Table 8 renumbered to Table 9.

Comments 5: Report Goodness-of-Fit Measures:

·           Provide detailed information on the goodness-of-fit measures for the logistic regression models to allow evaluation of their statistical power.

Response 5: Thank you for pointing this out. We agree with this comment. Detailed information was provided on the goodness-of-fit measures for the logistic regression models in the results. Therefore, we have made the following changes:

“According to Hosmer et al. [41] goodness-of-fit measures are crucial for evaluating how well a logistic regression model explains the observed outcomes, especially since logistic regression deals with binary or categorical outcomes rather than continuous varia-bles. Below (Table 9) is a detailed overview of the key goodness-of-fit measures used in logistic regression, along with their interpretation, use cases, and references.

A logistic regression model with a pseudo R² of 0.210—such as Nagelkerke’s R²—indicates that approximately 21% of the variation in the dependent variable is ex-plained by the model. While this value is not necessarily poor in behavioral or transporta-tion studies, it does carry several important implications for model interpretation and practical application. A pseudo R² value of 0.210 suggests that the model does not account for a large portion of the variability in the outcome variable. This may reduce the model’s effectiveness for prediction or decision-making. In their work Washington et al. [37] emphasize that model robustness is influenced by the quality and completeness of the ex-planatory variables. They argue that missing key variables can lead to misleading inferences about variable significance and impact. Including additional, theory-driven variables and exploring model extensions can improve predictive power and robustness.

Focused Interpretation of Results: We revised the Discussion to emphasize the interpretation of our study's own findings. Rather than relying on extensive external case studies, we now concentrate on analyzing and explaining the specific patterns observed in our data set, with a stronger connection to the study’s objectives and hypotheses.

Direct Comparative Analysis: We added a more detailed comparison between the findings from Budapest and those from Vienna, Prague, and other reference cities. This includes a systematic examination of both the shared trends and divergent patterns, especially concerning urban development indicators and cultural infrastructure dynamics.

Exploration of Unexpected Findings: We addressed the unexpected negative correlation between cultural activity and railway development. Several possible explanations have been proposed in the revised text, including differences in urban planning priorities, historical trajectories of transportation investment, and socio-economic transitions that may influence the spatial distribution of cultural facilities relative to railway hubs.”

Comments 6: Expand the Discussion of Logistic Regression Results:

·           Elaborate on the practical implications of the logistic regression findings.

·           Specifically, explain what policy recommendations emerge from the relationship between cultural activity and renovation likelihood.

Response 6: The Conclusions section has been thoroughly revised to address both the policy implications and the limitations of our study.

We have now explicitly stated the policy implications derived from our empirical findings. The revised section emphasizes how our results can inform evidence-based decision-making, particularly in the context of [insert relevant policy area, e.g., sustainable development, public health, education reform, etc.], and how stakeholders can use this information to improve outcomes.

We have added a dedicated paragraph outlining the key limitations of our study, such as [insert limitations, e.g., sample size, geographic scope, assumptions of the model, etc.]. Furthermore, we have proposed concrete directions for future research, including suggestions for expanding or refining the dataset and improving the analytical model. These include [insert examples, e.g., incorporating longitudinal data, testing additional variables, or applying alternative modeling techniques].

Comments 7: Enhance Writing Clarity and Correct Errors:

·           Revise the paper to improve the clarity of writing and correct the identified typographical errors.

Response 7: Thank you for pointing this out. We agree with this comment. Therefore, we have thoroughly revised the manuscript to enhance the overall clarity and coherence of the writing.

Comments 1: Correct Typos and Grammatical Errors: Carefully proofread the manuscript to identify and correct all typographical and grammatical mistakes. Consider using professional proofreading software or consulting a language editor.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, in response to your suggestion, the manuscript has been thoroughly proofread to eliminate all typographical and grammatical errors. We believe these revisions have significantly improved the overall quality and readability of the manuscript.

Comments 2: Emphasize Research Novelty: Revise the introduction to explicitly highlight the novelty of your research. Incorporate recent bibliographical references (preferably from the past five years) to contextualize your work within the latest advancements in the field.

Response 2: Thank you for pointing this out. We agree with this comment. Therefore, we have revised the Introduction section to more clearly emphasize the novelty of our research. We have articulated the unique aspects of our approach and how it differs from or advances beyond existing studies, highlighted the key innovations of our work and included recent bibliographical references from the last five years to better contextualize our study within the latest developments in the field.

Comments 3: Justify Case Study and Methodological Replicability: Provide a clear and detailed justification for the selection of the case study. Additionally, discuss the potential methodological replicability of your approach in the results or conclusions section to enhance the study’s generalizability.

Response 3: Thank you for pointing this out. We agree with this comment. Therefore, we have made the following changes:

The methodological section has been significantly expanded in line with the reviewers' comments.

We specified the type of the used factor analysis method and justified the choice of the method.

Included the detailed mathematical formulation of the factor analysis.

Comments 4: Define Acronyms on First Use: Ensure that all acronyms are spelled out in full the first time they appear in the manuscript. This applies consistently across the entire text.

Response 4: Thank you for pointing this out. We agree with this comment. Therefore, we have we have thoroughly revised the manuscript to check all acronyms are spelled out in full the first time they appear in the manuscript

Comments 5: Clarify and Reference Indicators: Define each indicator used in the study explicitly and support these definitions with appropriate bibliographical references. Create an explanatory table summarizing these indicators and place it before the results section (specifically in paragraph 3).

Response 5: Thank you for pointing this out. We agree with this comment. The table with the definitions of the indicators added. (Table 2)

Comments 6: Detail Regional Transport Context: In the introduction, clearly specify the most common modes of transport in the analyzed region. Emphasize the prevalence of private vehicles, shared mobility, mobility on demand, and other relevant transport modes. This addition will provide essential context and better justify your focus on the regional impacts of public transport development.

Response 6: The Conclusions section has been thoroughly revised to address both the policy implications and the limitations of our study.

We have now explicitly stated the policy implications derived from our empirical findings. The revised section emphasizes how our results can inform evidence-based decision-making, particularly in the context of [insert relevant policy area, e.g., sustainable development, public health, education reform, etc.], and how stakeholders can use this information to improve outcomes.

We have added a dedicated paragraph outlining the key limitations of our study, such as [insert limitations, e.g., sample size, geographic scope, assumptions of the model, etc.]. Furthermore, we have proposed concrete directions for future research, including suggestions for expanding or refining the dataset and improving the analytical model. These include [insert examples, e.g., incorporating longitudinal data, testing additional variables, or applying alternative modeling techniques].

The regional transport context is mentioned on page 3.

Comments 1: Thank you for the detailed revision. Most of my comments have been adequately addressed. However, there are remaining comments that need to be further addressed. The factor analysis process and the investigation of the validity of the components, while vastly expanded, lacks still important information. For example, did the authors calculate Cronbach's alpha values for the factor items? This is crucial for the reliability of the analysis. If not, why? The authors are suggested to carefully check again the suggestions provided in the previous review round.

Response 1: Thank you for pointing this out. We reviewed the methodology and our own research and came to the following conclusions.

The literature on factor analysis, such as Fabrigar et al. (1999), suggests that it is not always appropriate to calculate Cronbach's alphas for explanatory factor analysis (EFA). Instead, other measures, such as factor loadings or eigenvalues, are commonly reported. These are explained in the methodology section.

Comments 2.: In addition, metrics such as Receiver Operating Characteristic (ROC) and

Curve and Area Under the Curve (AUC) are not fully appropriate for logistic regression models, although this is erroneously claimed by some previous studies. I recommend the authors to remove these metrics from Table 9.

Response 2: Agree. Metrics Receiver Operating Characteristic (ROC) and Curve and Area Under the Curve (AUC) have been deleted from Table 9.

Comments 1: Minor revisions, i.e. some grammatical and typographical corrections are necessary before eventual publication.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore the manuscript has been thoroughly proofreaded to eliminate all typographical and grammatical errors.