Unsupervised Learning for Topological Classification of Transportation
  Networks
Sina Sabzekar Mohammad Reza Valipour Malakshah Zahra Amini
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.