PONE-D-25-37604

Effectiveness of Roadside Alcohol Testing in Reducing Fatal Accidents and Fatal Drinking-Driving Accidents: A Multi-City Study in China

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All the required documents are ready for submission. The funding source has been included in the cover letter.

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Reply

Thank you for your notice regarding the potential copyright issue for Figure 1.

We confirm that the base map in Figure 1 was obtained from the Standard Map Service System of the Ministry of Natural Resources of the People's Republic of China, which is an official and publicly available government data source. According to the website,

“标准地图依据中国和世界各国国界线画法标准编制而成�可用于新闻宣传用图、书刊报纸插图、广告展示背景图、工艺品设计底图等�也可作为编制公开版地图的参考底图。”

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The dataset is openly accessible through the following official website:

http://bzdt.ch.mnr.gov.cn/download.html?searchText=GS(2019)1822%EF%BC%89

We used this publicly available government map as the base layer and overlaid population data derived from the national census to produce Figure 1. The final figure is an original composite created by the authors, not a reproduction of any copyrighted or proprietary map (such as Google Maps, Street View, or Earth).

Therefore, there is no copyright infringement concern, and the figure fully complies with the CC BY 4.0 license requirements for open-access publication.

In the revised manuscript, we have clarified this in the figure caption as follows:

Fig. 1. Study regions and city population. Downloaded from the Standard Map Service System of the Ministry of Natural Resources of the People's Republic of China (http://bzdt.ch.mnr.gov.cn/). The base map was freely provided for public use and modified by the authors to include population data from the national census.

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

Reply

The original data can be accessed from the following public source:

Hu D, Chen X, Liu L. (2024). Full judgment document data for intelligent trial assistance [面向智能审判辅助的全量裁判文书数据]. National Big Data Service Platform [全国数据服务平台]. Available from: https://www.nbsdc.cn/general/dataDetail?id=66606e34195d266d328f3ca0&type=1

In addition, the codes used for data mining and the datasets applied in the VAR/IRF models and XGBoost with SHAP analyses have been included as supplementary files with this submission. These materials ensure that all analyses can be replicated and verified in accordance with PLOS ONE’s data availability policy.

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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: In the manuscript titled “Effectiveness of Roadside Alcohol Testing in Reducing Fatal Accidents and Fatal Drinking-Driving Accidents: A Multi-City Study in China”, the authors analyzed a large-scale dataset (248 cities, 2014–2020) and applied both VAR/IRF models and XGBoost with SHAP analysis to investigate the dynamic effects of roadside alcohol testing on traffic mortality. This study contains interesting findings and provides valuable evidence for understanding the deterrent effects of DUI enforcement, especially the heterogeneous effects between small and large cities.

However, the manuscript also presents some weaknesses in terms of structure, literature review depth, and data/method transparency. Therefore, major revision has to be done before this manuscript could be accepted for publication in PLOS ONE.

1. The transition between literature review and research gap is not sufficiently clear. The authors should streamline this part and highlight the novelty of their study more explicitly.

2. The review cites many studies, but some are descriptive rather than analytical. Please strengthen the discussion with recent works on machine learning applications in traffic safety and international comparisons of enforcement strategies. I suggest the author integrate these articles:Sight distance analysis of vehicles with driving automation on horizontal curves of as-built highway tunnels; A system theory based accident analysis model: STAMP-fuzzy DEMATEL; Temporal heterogeneity in traffic crash delays: causal inference from multi-scale time factors and sample-wise structural decomposition. So, I believe this research will be greatly improved.

Reply

To add the transition between literature review and research gap, we have read the three articles and added a new paragraph to increase the smooth transition:

“Previous studies have primarily relied on traditional time-series approaches, such as interrupted time series analysis, which can only compare changes in mortality trends between two time periods and are therefore typically applied to evaluate pre- and post-legislation effects. In contrast, mixed-effects regression models can accommodate hierarchical structures and control for observed confounders [14]; however, they generally impose a linearity assumption that may not adequately capture the complex relationship between enforcement intensity and traffic mortality. Moreover, traffic accident data often exhibit weak temporal dependence and non-stationary characteristics, making traditional time-series modeling challenging [17]. Therefore, it is necessary to complement conventional regression approaches with machine learning models that relax the linearity assumption, enabling a more flexible assessment of enforcement impacts while more effectively addressing confounding factors [18]. In addition, the use of SHapley Additive exPlanations (SHAP) values provides an interpretable framework to quantify the contribution of enforcement, among a set of potential confounders, to variations in mortality rates [19].”

3. The reliance on court judgment documents introduces potential selection bias (e.g., only cases with on-site deaths and surviving offenders are included). This limitation should be emphasized more clearly in the methodology and discussion.

Reply

In the first paragraph of the Discussion, we have stated that:

“It is important to note that court system data may underreport traffic-related mortalities in China, as court records typically include only cases in which the victim died at the scene and the offender survived. In contrast, the health department is able to capture fatalities that occur after prolonged hospitalization, as well as deaths of both victims and offenders [23]. Despite this potential underreporting, the number of cases retrieved from court documents remains substantial, underscoring the importance of investigating whether increased frequency of roadside checks contributes to a reduction in traffic-related fatalities.”

4. Only XGBoost was employed in the machine learning part. Please justify why no comparison with other algorithms (e.g., Random Forest, LSTM) was attempted. This would enhance methodological robustness.

Reply

We appreciate the reviewer’s insightful comment. The three papers mentioned indeed focused mainly on engineering applications, where machine learning models are optimized for predictive accuracy to enhance traffic or engineering safety. In contrast, the current study emphasizes the policy dimension. Specifically, the effectiveness of law enforcement policies. Therefore, our modeling objective differs from that of engineering-oriented research.

Unlike engineering studies that pursue highly precise predictive performance, the mathematical modeling of law enforcement policy does not necessarily benefit from such optimization. Even if a model achieves the best performance on the current dataset, it may not generalize well under different political systems and cultural contexts, resulting in potential overfitting. Consequently, in this study we selected the XGBoost model from three candidate algorithms through a grid search for optimal hyperparameters, without exhaustively testing all machine learning methods or tuning a larger set of parameters. This decision reflects our intention to balance model interpretability and generalizability rather than to over-optimize performance on a single dataset.

In the Method part, we added:

“To account for confounding effects that traditional time-series methods cannot adequately address, we employed Gradient Boosted Regression Trees (GBRT) implemented via the XGBoost library. Before finalizing the model, we compared three candidate algorithms, namely Random Forest, Support Vector Regression, and XGBoost. A grid search procedure was used to identify the optimal set of hyperparameters for each model. Based on both predictive performance and interpretability considerations, XGBoost was selected as the final model because it provided an appropriate balance between coefficient of determination (R²) and root mean squared error (RMSE).”

In the limitation, we emphasized that:

“This study is based on data from China, and the results may not be directly generalizable to other countri

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