Decoding street network morphologies and their correlation to travel mode choice

TL;DR

Riascos-Goyes et al. investigate how street-network morphology conditions travel mode choice by classifying urban form across nine U.S. metros using 17 spatial/topological indicators organized into four morphologic dimensions. They combine a theory-driven MADM typology classification with unsupervised PCA-K-means clustering to identify canonical patterns and internal subpatterns, then relate these morphologies to modal shares via descriptive statistics, marginal effects, and nonparametric post hoc tests. The study finds that grid-like morphologies support higher shares of active and public transport while organic and cul-de-sac forms align with higher private car use, with effects significant at $p<1\times10^{-19}$ and accompanied by meaningful effect sizes. By providing a reproducible framework for morphology-based mobility analysis, the work advances urban planning practice and highlights the value of integrating spatial typologies into mobility policy, while noting context-specific heterogeneity and the need for broader geographic validation.

Abstract

Urban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Building on theoretical principles of urban structure and advances in unsupervised learning, we systematically classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration. The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing. Here we show that distinct urban forms are systematically associated with different mobility behaviors, such as reticular morphologies being linked to significantly higher public transport use (marginal effect = 0.49) and reduced car dependence (-0.41), while organic forms are associated with increased car usage (0.44), and substantial declines in public transport (-0.47) and active mobility (-0.30). These effects are statistically robust (p < 1e-19), highlighting that the spatial configuration of urban areas plays a fundamental role in shaping transportation choices. Our findings extend previous work by offering a reproducible framework for classifying urban form and demonstrate the added value of morphological analysis in comparative urban research. These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.

Figures (5)

• Figure 1: Illustrative cases of canonical urban street typologies. (Produced by the author using OpenStreetMap data).
• Figure 2: Methodological framework for typology classification and subpattern discovery. The process integrates spatial and topological metrics through theoretical classification (MADM) and unsupervised clustering to identify urban typologies and their internal subpatterns.
• Figure 3: Morphological classification of street networks. Top row (a1–c1): primary typologies. Bottom row (a2–c2): sub-patterns identified within each category via unsupervised learning.
• Figure 4: Ridgeline plot showing the distribution of modal shares (active, public, and private) across urban morphological patterns. Each ridge represents a density estimate for a specific typology.
• Figure 5: Heatmap of the marginal effects of mobility modes across urban layout patterns.