With increasing urbanization, transportation plays an increasingly critical role in city development. The number of studies on modeling, optimization, simulation, and data analysis of transportation systems is on the rise. Many of these studies utilize transportation test networks to represent real-world transportation systems in urban areas, examining the efficacy of their proposed approaches. Each of these networks exhibits unique characteristics in their topology, making their applications distinct for various study objectives. Despite their widespread use in research, there is a lack of comprehensive study addressing the classification of these networks based on their topological characteristics. This study aims to fill this gap by employing unsupervised learning methods, particularly clustering. We present a comprehensive framework for evaluating various topological network characteristics. Additionally, we employ two dimensionality reduction techniques, namely Principal Component Analysis (PCA) and Isometric Feature Mapping (ISOMAP), to reduce overlaps of highly correlated features and enhance the interpretability of the subsequent classification results. We then utilize two clustering algorithms, K-means and HDBSCAN, to classify 14 transportation networks. The PCA method, followed by the K-means clustering approach, outperforms other alternatives with a Silhouette score of $0.510$, enabling the classification of transportation networks into five clusters. We also provide a detailed discussion on the resulting classification.

对城市交通系统的建模、优化、仿真和数据分析的研究逐渐增多。本研究采用无监督学习方法，尤其是聚类方法，通过综合评估交通网络的拓扑特征，利用主成分分析和等度映射减少特征之间的相关性，实现交通网络的分类。结果表明，PCA方法和K-means聚类方法最为有效，使交通网络能够被划分为五个簇，并提供了相关分类讨论。

交通网络的无监督学习拓扑分类