Academic Journal
Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.
| Τίτλος: | Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning. |
|---|---|
| Συγγραφείς: | Lee EJ; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea., Kim S; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea.; The Korea Transport Institute, Sejong-si, Sejong, Republic of Korea., Lee HJ; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea., Jhong JH; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea. |
| Πηγή: | PloS one [PLoS One] 2026 Jun 22; Vol. 21 (6), pp. e0350616. Date of Electronic Publication: 2026 Jun 22 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Accidents, Traffic*/statistics & numerical data , Accidents, Traffic*/prevention & control , Boosting Machine Learning Algorithms*, Automobile Driving/legislation & jurisprudence ; Humans ; Logistic Models ; Predictive Learning Models ; Random Forest ; Republic of Korea ; Risk Factors |
| Περίληψη: | Rapid urbanization and increasing traffic volumes have made the occurrence of traffic crashes and the resulting harm a major public safety concern. This study analyzes traffic crash data from Cheongju, a mid-sized city in Chungcheongbuk-do, to identify key determinants of crash severity and provide evidence-based policy recommendations. Our approach is novel in that it integrates statistical modeling and machine learning methodologies; this dual approach not only overcomes the limitations inherent in using either technique alone but also allows for the identification of consistent risk factors influencing traffic crash severity that may have gone unrecognized otherwise. Marginal effects of explanatory variables were interpreted using ordinal logistic regression, while feature importance in machine learning models-including Support Vector Machine, Random Forest, XGBoost, and LightGBM-was evaluated using SHAP (SHapley Additive exPlanations) values. Both analytical approaches consistently identified traffic signal violations, failure to comply with safe driving obligations, and the absence of a median barrier on undivided roads as significant predictors of crash severity. By leveraging empirical data specific to Cheongju, our research provides regionally tailored insights that distinguish our work from prior studies with broader or less localized focus. These findings highlight the need for stricter enforcement of traffic regulations and structural improvements in roadway infrastructure and can inform policymakers in formulating effective, context-specific measures to enhance road safety. (Copyright: © 2026 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| Entry Date(s): | Date Created: 20260622 Date Completed: 20260622 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13286193 |
| DOI: | 10.1371/journal.pone.0350616 |
| PMID: | 42329879 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
|---|---|
| DOI: | 10.1371/journal.pone.0350616 |