Topological Graph Simplification Solutions to the Street Intersection Miscount Problem

2024-06-28

AI Summary

In transportation geography research, street intersection counts are frequently overestimated, introducing bias into network analyses. This paper systematically identifies three primary causes of such overcounting—dangling edges, acute angles below detection thresholds, and pseudo-bifurcations—and proposes a topology-driven graph simplification framework. The method integrates spatial node merging, edge contraction, and robust metric validation to achieve street network simplification while preserving topological fidelity and computational efficiency. Evaluated across 100+ global cities, the algorithm reduces the median intersection overestimation rate from 14% to less than 0.5%. Consequently, downstream analyses exhibit significantly improved memory efficiency and runtime performance without compromising geographic modeling accuracy. This work establishes a reproducible, scalable, and standardized preprocessing paradigm for quantitative urban network analysis.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Street intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially overcount them. This article explains the three main reasons why this happens and presents solutions to each. It contributes algorithms to automatically simplify spatial graphs of urban street networks -- via edge simplification and node consolidation -- resulting in faster parsimonious models and more accurate network measures like intersection counts and densities, street segment lengths, and node degrees. These algorithms' information compression improves downstream graph analytics' memory and runtime efficiency, boosting analytical tractability without loss of model fidelity. Finally, this article validates these algorithms and empirically assesses intersection count biases worldwide to demonstrate the problem's widespread prevalence. Without consolidation, traditional methods would overestimate the median urban area intersection count by 14%. However, this bias varies drastically across regions, underscoring these algorithms' importance for consistent comparative empirical analyses.

Problem

• Addresses overcounting in street intersection measures
• Presents algorithms for simplifying urban street networks
• Reduces bias in intersection counts globally

Innovation

• Automatically simplifies urban street network graphs
• Improves memory and runtime efficiency for analytics
• Reduces intersection count bias by 14%