School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Key Laboratory for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China.
Chaos. 2023 Jul 1;33(7). doi: 10.1063/5.0145079.
Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%-28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.
城市道路网络（URN）作为城市的简化视图和重要组成部分，具有不同的结构，导致交通效率、可达性、弹性和许多社会经济指标的水平也各不相同。因此，URN 的拓扑特征在文献中受到了极大的关注，而现有研究已经使用了各种边界来提取 URN 进行分析。这自然就引出了一个问题，即使用小尺寸边界得出的拓扑模式是否与使用常用的行政边界或日常出行范围边界得出的模式一致。本文进行了大规模的实证分析，以揭示边界效应对中国大陆 363 个城市 URN 的 22 个拓扑指标的影响。统计结果表明，边界对平均节点度、路段的边缘密度、方向熵、最短或最快路径的偏心率几乎没有影响，而其他指标，包括聚类系数、高级路段的比例、平均边长度以及与路线相关的指标，如平均角度偏差，在使用不同边界提取的道路网络之间存在显著差异。此外，使用不同边界识别的高中心性组件在位置上存在显著差异，行政边界和日常出行范围边界提取的道路网络之间只有 21%-28%的高中心性节点重叠。这些发现为城市规划提供了有用的见解，有助于更好地预测道路网络结构对人员流动和社会经济活动流动的影响，特别是在快速城市化和道路网络不断扩张的背景下。
