Beyond Centrality: Understanding Urban Street Network Typologies Through Intersection Patterns

• URL: http://arxiv.org/abs/2511.06747v1
• Date: Mon, 10 Nov 2025 06:22:05 GMT
• Title: Beyond Centrality: Understanding Urban Street Network Typologies Through Intersection Patterns
• Authors: Anu Kuncheria, Joan L. Walker, Jane Macfarlane,
• Abstract summary: This study examines over 100 cities in the San Francisco Bay Area.<n>We introduce a novel metric for classifying intersections, distinguishing between different types of 3-way and 4-way intersections.<n>Through the application of clustering algorithms in machine learning, we have identified three distinct typologies.
• Score: 1.422288795020666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The structure of road networks plays a pivotal role in shaping transportation dynamics. It also provides insights into how drivers experience city streets and helps uncover each urban environment's unique characteristics and challenges. Consequently, characterizing cities based on their road network patterns can facilitate the identification of similarities and differences, informing collaborative traffic management strategies, particularly at a regional scale. While previous studies have investigated global network patterns for cities, they have often overlooked detailed characterizations within a single large urban region. Additionally, most existing research uses metrics like degree, centrality, orientation, etc., and misses the nuances of street networks at the intersection level, specifically the geometric angles formed by links at intersections, which could offer a more refined feature for characterization. To address these gaps, this study examines over 100 cities in the San Francisco Bay Area. We introduce a novel metric for classifying intersections, distinguishing between different types of 3-way and 4-way intersections based on the angles formed at the intersections. Through the application of clustering algorithms in machine learning, we have identified three distinct typologies - grid, orthogonal, and organic cities - within the San Francisco Bay Area. We demonstrate the effectiveness of the metric in capturing the differences between cities based on street and intersection patterns. The typologies generated in this study could offer valuable support for city planners and policymakers in crafting a range of practical strategies tailored to the complexities of each city's road network, covering aspects such as evacuation plans, traffic signage placements, and traffic signal control.

Related papers

• A computational framework for quantifying route diversification in road networks [1.9232329432211988]
  We examine how road network structure shapes the counterpart to traffic concentration: route diversification.<n>We introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes.
  arXiv  Detail & Related papers  (2025-10-02T21:41:53Z)
• Urban Forms Across Continents: A Data-Driven Comparison of Lausanne and Philadelphia [7.693465097015469]
  This study presents a data-driven framework to identify and compare urban typologies across geographically and culturally distinct cities.<n>We extracted multidimensional features related to topography, multimodality, green spaces, and points of interest for the cities of Lausanne, Switzerland, and Philadelphia, USA.<n>The results reveal coherent and interpretable urban typologies within each city, with some cluster types emerging across both cities despite their differences in scale, density, and cultural context.
  arXiv  Detail & Related papers  (2025-05-05T18:13:22Z)
• Understanding the Structure and Resilience of the Brazilian Federal Road Network Through Network Science [44.99833362998488]
  This paper models the federal road network in Brazil as weighted networks.<n>We aim to unveil its topological characteristics and identify key locations (cities) that play important roles for the country through 75,000 kilometres of roads.<n>Our findings aim to bring clarity to the overall structure of federal roads in Brazil, thus providing actionable insights for improving infrastructure planning and prioritising resources to enhance network resilience.
  arXiv  Detail & Related papers  (2024-12-20T13:02:50Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• A shape-based heuristic for the detection of urban block artifacts in street networks [0.0]
  Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place. Their graph representation is often designed primarily for transportation purposes. This representation is less suitable for other use cases where transportation networks need to be simplified.
  arXiv  Detail & Related papers  (2023-09-01T13:11:35Z)
• Graph-based Topology Reasoning for Driving Scenes [102.35885039110057]
  We present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2.
  arXiv  Detail & Related papers  (2023-04-11T15:23:29Z)
• Extracting Spatial Interaction Patterns between Urban Road Networks and Mixed Functions [4.198538504785438]
  The more mixed the functions of an area has, the more possible its vitality may be. Our study shows that the higher the degree of the road network structure has, the more likely it will attract functions' aggregation. It also reveals that diversified local degree will help gather urban functions.
  arXiv  Detail & Related papers  (2022-11-03T01:41:46Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Local Betweenness Centrality Analysis of 30 European Cities [0.0]
  We show that road segments important for long-distance transportations are concentrated along larger streets while those for short range transportations form clusters around CBD, historical, or residential districts. Our findings are useful for urban planners and decision-makers to understand the current situation of the city and make informed decisions.
  arXiv  Detail & Related papers  (2021-03-21T17:08:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.