蔡先华, 刘凯丽, 胡卓良, 张远. 利用街区面块拓扑构建道路网络的算法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348
引用本文: 蔡先华, 刘凯丽, 胡卓良, 张远. 利用街区面块拓扑构建道路网络的算法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348
CAI Xianhua, LIU Kaili, HU Zhuoliang, ZHANG Yuan. An Algorithm for Constructing Road Network Using Block Polygon Topology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348
Citation: CAI Xianhua, LIU Kaili, HU Zhuoliang, ZHANG Yuan. An Algorithm for Constructing Road Network Using Block Polygon Topology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348

利用街区面块拓扑构建道路网络的算法

An Algorithm for Constructing Road Network Using Block Polygon Topology

  • 摘要: 道路交通网络是进行各种道路交通网络分析与可视化的基础。构建道路网络的常用方法是运用已有道路面矢量数据提取道路中心线,并自动生成道路网络。提出了一种根据街区面块拓扑关系自动构建道路网络的算法,首先,根据道路面求反得到街区面块并计算街区面块间的拓扑关系;然后,根据街区面块之间的拓扑关系自动建立道路网络拓扑关系;最后,计算路段(网络弧段)中心线和道路交叉口(节点)的几何位置,完成数字道路网络的构建。与以住算法不同,该算法将拓扑关系构建与中心线提取分开,直接由道路面原始数据构建网络拓扑关系,保证拓扑结构的准确性,且为道路中心线提取提供路段交叉口判别依据。实验表明,所提出算法较好地解决了已有算法在自动计算道路中心线时数据预处理复杂和道路面分割难以处理等问题。

     

    Abstract:
      Objectives  Road network is the basis of various road traffic analysis and visualization. It is difficult to extract road centerlines automatically in analyzing the internal of complex polygonal road surface and difficult road surface segmentation.An algorithm of constructing road network is proposed to distinguish road sections and intersections, which can simplify data preprocessing based on the topological relationship among blocks.
      Methods  Firstly, through performing logical negation operation on road surface, complex polygons with holes are converted into independent and simple block polygons. Secondly, neighbor points among them are calculated, and neighboring blocks of each block can be obtained using the neighbor analysis. Consequently, the topological relationship among common road sections or common intersections is determined, and road sections and intersections are discriminated. Then, it is obtained that two common blocks on both sides of each section and all blocks which around each intersection, so the geometric positions of centerlines of road sections are calculated by means of applying the unit circle rolling tracking algorithm. Finally, according to the type of intersection, the positions of intersection associated with road sections are determined to complete the construction of the road network digital model.The algorithm is verified by developing experimental software which uses urban road surface data in a city topographic map with scale of 1∶1 000 as experimental data.
      Results  The experimental result shows that the topological relationship of the final road traffic network is accurate and the matching relationship among blocks is correct. The positions of road centerlines are accurate and every line shape is smooth. There are no excess centerlines or missing centerlines. The proposed method works well in dense and sparse neighborhoods. Compared with traditional algorithms, the algorithm for constructing road network by using street topology relationship doesn't need to perform manual segmentation or road surface edge line encryption. It simplifies complex graphics by negating the road surface and obtains road sections information according to topological relationship among polygons. The road centerlines are calculated by using unit circle rolling algorithm, so the geometric characteristic of the road centerlines and the accuracy of the topological relationship can be guaranteed. The experimental result shows that the method construct a road network with complete topology, accurate location and smooth shape.
      Conclusions  In the process of constructing the road section-block topology using the method, the road sections and intersections are distinguished. It solves the problems of segmentation of the road sections and provides a good basis for the extraction of road centerlines. It is suitable for the construction of geometric road networks in dense road networks and dense blocks.

     

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