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April 30, 2026Journal of Korean Society of TransportationOpen Access

Prediction of Road Maintenance Intervention and Assessment of Influencing Factors with ML-SHAP: Focusing on Road Traffic Conditions in Jeju

Yeonwoo SEO Taeseong Kim Gyugeun Yoon Young Joon Lee

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Key Points

The aim is to predict future maintenance needs for roads in Jeju by analyzing various influencing factors.
Developed a dataset combining annual road, traffic, and weather data for national and local roads
Built a machine learning model to predict future road maintenance occurrences
Applied SHAP to interpret the importance of factors affecting maintenance needs
Generated quantitative evidence to identify road segments with high potential for maintenance needs
Highlighted critical factors influencing pavement performance in varying traffic and climate conditions
Provided insights for prioritizing budget allocation and selection of maintenance targets in Jeju

Implication

Study predicts road maintenance needs in Jeju, suggesting actionable insights for infrastructure management.

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Cite This Study

SEO et al. (2026) studied this question.

synapsesocial.com/papers/6a0021cdc8f74e3340f9cb4e https://doi.org/10.7470/jkst.2026.44.2.289

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