Safety management and operation of intercity roads require understanding the behavioral differences among network corridors. Simple ranking of high-risk corridors, while useful for prioritization, fails to reveal similar behavioral patterns in terms of traffic volume, speed, fleet composition, and violation profiles. This study aims to cluster the intercity corridors of Razavi Khorasan Province based on traffic count data from the 141 system in the year 1404 (2025–2026). In this study, 136 unidirectional corridors were included in the analysis after data quality control and aggregation of corridor-level features. The features used included annual traffic volume, following-distance violation rate, speed violation rate, overtaking violation rate, weighted average speed, and the share of commercial/heavy vehicles. To reduce scaling effects, the data were standardized, and Principal Component Analysis was employed to visualize the data structure. Subsequently, two approaches—K-Means and Ward's hierarchical clustering—were evaluated for cluster counts ranging from 2 to 7. Based on the Silhouette index, k=4 with a coefficient of 0.269 provided the best K-Means configuration. The results revealed four behavioral types: "high-risk with high following-distance violations," "high-traffic with moderate behavioral risk," "commercial/heavy with controlled speed," and "lower-risk and more stable." The first cluster, comprising 47 corridors, had an average following-distance violation rate of 205.55 violations per 1,000 vehicles and a weighted rate of 244.92, placing it as the top priority for safety monitoring and intervention. The findings of this study indicate that data-driven clustering can serve as a suitable complement to risk indices and predictive models, assisting road authorities in managing similar corridors through more coordinated policies.