Modern Transportation and Metallurgical Materials

With the continuous growth of expressway traffic volume and diversified structures of travel demand， traditional decision-making methods for capacity expansion fail to meet the requirements of refined traffic management. Against the background of expressway construction， this paper proposes a hierarchical and classification analytical framework for capacity expansion demand evaluation. Starting from core traffic indicators including traffic flow， vehicle speed， traffic density and peak-hour characteristics， Principal Component Analysis （PCA） is adopted to extract key factors representing segmentlevel expansion pressure， which eliminates multicollinearity interference among multiple indicators.The K-Means clustering algorithm is further applied to conduct horizontal categorization of road segments， dividing expressway sections into three types： capacity-oriented segments， uneven-flow segments， and segments with no expansion demand. This classification reveals the disparities among road segments in terms of traffic load and peak concentration characteristics. Combined with the Analytic Hierarchy Process （AHP）， a vertical hierarchical grading system is constructed from two dimensions：traffic capacity and flow distribution. Comprehensive evaluation scores are quantitatively calculated，and four expansion demand grades （low， medium-low， medium-high， high） are determined via the quantile method.Empirical verification results demonstrate that the proposed framework can effectively identify road segments with high expansion priority and provide scientific support for resource allocation and traffic governance strategies. Compared with conventional expansion judgment approaches relying on single indicators， the hierarchical classification framework developed in this study can comprehensively reflect the operational pressure and potential bottleneck features of expressways， offering novel insights for expressway planning， capacity expansion and operational management.