# Unsupervised Learning for Topological Classification of Transportation Networks

arXiv: https://arxiv.org/abs/2311.13887 · HTML: https://mlsanity.com/paper/2311.13887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 76 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network](https://mlsanity.com/paper/2202.10508/) (2022)
- [Topological street-network characterization through feature-vector and cluster analysis](https://mlsanity.com/paper/1806.07952/) (2018)
- [Day-to-Day Dynamic Traffic Assignment with Imperfect Information, Bounded Rationality and Information Sharing](https://mlsanity.com/paper/1811.10729/) (2018)
- [A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility](https://mlsanity.com/paper/2301.09758/) (2023)

## Abstract (arXiv)

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.