马京振, 孙群, 温伯威, 周炤, 陆川伟, 吕峥, 孙士杰. 结合轨迹数据的混合多特征道路网选取方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(7): 1009-1016. DOI: 10.13203/j.whugis20190480
引用本文: 马京振, 孙群, 温伯威, 周炤, 陆川伟, 吕峥, 孙士杰. 结合轨迹数据的混合多特征道路网选取方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(7): 1009-1016. DOI: 10.13203/j.whugis20190480
MA Jingzhen, SUN Qun, WEN Bowei, ZHOU Zhao, LU Chuanwei, LÜ Zheng, SUN Shijie. A Hybrid Multi-feature Road Network Selection Method Based on Trajectory Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(7): 1009-1016. DOI: 10.13203/j.whugis20190480
Citation: MA Jingzhen, SUN Qun, WEN Bowei, ZHOU Zhao, LU Chuanwei, LÜ Zheng, SUN Shijie. A Hybrid Multi-feature Road Network Selection Method Based on Trajectory Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(7): 1009-1016. DOI: 10.13203/j.whugis20190480

结合轨迹数据的混合多特征道路网选取方法

A Hybrid Multi-feature Road Network Selection Method Based on Trajectory Data

  • 摘要: 道路网选取是制图综合的重要内容,针对现有方法仅考虑道路网静态特征等问题,提出了一种结合轨迹数据的混合多特征选取方法。首先以stroke为基本选取单元,构建对偶图来描述路网的结构关系,采用长度、连通度、接近度和中介度等指标对道路的静态特征进行评价;然后结合轨迹数据特点,采用车流量、车辆速度和道路交叉口附近的车辆密度等指标对道路的动态特征进行评价;最后利用基于相互关系准则的标准重要性方法计算得到各指标的权值及各道路的综合重要性值。同时引入线Voronoi图对道路进行划分,得到道路的密度特征值,并将其作为道路网选取的约束指标。实验结果表明,所提方法能够保持道路的整体结构,同时顾及道路的连通性和密度分布,而且结合了轨迹数据的动态交通特性,选取结果符合实际情况,具有一定的实用性。

     

    Abstract:
      Objectives  Road network selection is an important part of cartographic generalization and the existing selection methods only consider the static characteristics of road network. A hybrid multi-feature selection method based on trajectory data is proposed to solve these problems.
      Methods  The proposed method takes stroke as the basic selection unit. Firstly, a dual graph is constructed to describe the structural relationship of the road network. The static characteristics of the road network are evaluated using such indicators as length, degree, betweenness and closeness. Secondly, based on the characteristics of the trajectory data, the dynamic characteristics of the road network are evaluated using such indicators as vehicle flow, vehicle speed and vehicle density near the road intersection. Finally, the weight of each indicator and the comprehensive importance value of each road are calculated by the method of criteria importance through intercriteria correlation. At the same time, the Voronoi diagram is introduced to divide the road network, and the density characteristics are obtained, which are taken as the constraint indicator of road network selection.
      Results  The experimental results show that this method can maintain the overall structure, taking into account the connectivity and density distribution, and can combine the traffic characteristics of the trajectory data.
      Conclusions  The selection results are in accordance with the actual situation and have certain practicability.

     

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