Unsupervised Learning for Topological Classification of Transportation Networks

TL;DR

This paper introduces an unsupervised learning framework combining PCA, ISOMAP, K-means, and HDBSCAN to classify transportation networks based on their topological features, aiding urban planning and network analysis.

Contribution

It presents a novel comprehensive framework for topological classification of transportation networks using unsupervised learning and dimensionality reduction techniques.

Findings

PCA combined with K-means achieved the best Silhouette score of 0.510.

The classification resulted in five distinct clusters of transportation networks.

The framework enhances interpretability and effectiveness of topological network analysis.

Abstract

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.