Title: Topological Graph Simplification Solutions to the Street Intersection Miscount Problem Show Chinese title

Title: 拓扑图简化方法解决街道交叉口误计数问题的解决方案

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. Show Chinese abstract

Abstract: 街道交叉口数量和密度是交通地理学和规划中的普遍测量指标。然而，典型的街道网络数据和典型的街道网络分析工具可能会大大高估这些指标。本文解释了发生这种情况的三个主要原因，并提出了针对每个问题的解决方案。它贡献了算法以自动简化城市街道网络的空间图——通过边简化和节点合并——从而得到更快且更简洁的模型以及更准确的网络测量，如交叉口数量和密度、街道段长度和节点度数。这些算法的信息压缩提高了下游图形分析的内存和运行时间效率，增强了分析的可处理性而不损失模型的真实性。最后，本文验证了这些算法，并通过实证评估全球范围内的交叉口数量偏差，以证明该问题的广泛存在。如果不进行合并，传统方法会高估中等城市区域的交叉口数量14\%。然而，这种偏差在不同地区差异很大，突显了这些算法对于一致的比较性实证分析的重要性。