Segment-based truck crash risk analysis on highways: clustering and logistic regression for safety enhancement

Truck-involved crashes on highways represent a significant threat to traffic safety because of their disproportionate severity and frequency. Existing crash analyses frequently overlook the spatial heterogeneity of highways by treating them as uniform networks, thereby limiting their effectiveness in identifying risk-prone areas. This study proposes a segment-level risk assessment framework that integrates machine-learning-based clustering with logistic regression modeling to evaluate truck crash patterns on South Korean expressways from 2020 to 2022.

Four clustering techniques-K-means, DBSCAN, Gaussian mixture model, and hierarchical clustering-were applied based on the crash frequency, severity metrics, and traffic exposure indicators. Among these, K-means clustering offered the most interpretable segmentation, identifying six distinct highway segment groups. Logistic regression was then used to examine the influence of various risk factors, such as driver fatigue, overloading, geometric features, and nighttime driving, on crash occurrences within each cluster.

The analysis revealed that crash risks varied considerably across segments: fatigue-related crashes were concentrated in freight-dense corridors; brake and overloading failures were more prevalent on steep or curved segments; and nighttime crashes were notably higher on port-access routes.

These findings support the implementation of targeted safety strategies tailored to local roadway characteristics, including rest-area expansion, automated fatigue detection, and lighting improvement. The proposed methodology offers a practical data-driven approach for transportation agencies seeking to prioritize high-risk highway segments and implement more effective crash prevention policies.