城市快速路横断面案例的聚类识别与关联规则挖掘研究

Clustering Identification and Association Rule Mining of Urban Expressway Cross-Section Cases

摘要: 针对城市快速路更新案例信息分散、工程规律难以提取的问题，本文对横断面案例进行聚类和关联规则挖掘，并利用快速路交通数据验证车道配置规律。结果表明，横断面案例可划分为5类典型模式，综合轮廓系数为0.534；地面辅道与非机动车道、高架主线和25~35 m横断面宽度之间存在较稳定的关联关系；交通量增加时，8车道和10车道方案的比例明显提高。研究结果可为快速路更新案例整理和初步方案分析提供参考。

Abstract: Urban expressway renewal cases are characterized by scattered information and difficulty in extracting engineering regularities. To address these issues, this study conducts clustering and association rule mining on cross-section cases and validates lane configuration patterns using expressway traffic data. The results show that the cross-section cases can be classified into five typical patterns, with an overall silhouette coefficient of 0.534. Relatively stable associations are identified among ground-level frontage roads, non-motorized vehicle lanes, elevated mainlines, and cross-section widths of 25~35 m. In addition, as traffic volume increases, the proportions of 8-lane and 10-lane schemes increase significantly. These findings provide a reference for the systematic compilation of expressway renewal cases and for preliminary scheme analysis.