Urban Road Network Matching Model Based on Fuzzy Hierarchy Theory and Its Application
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摘要: 随着国家城市群基础设施建设步伐的加快，对城市道路网数据现势性的需求急剧增加. 针对当前以位置和长度为主要参数的道路网匹配算法的不稳定性，以及分析结果的不确定性和可靠性差等问题，提出了一种新的基于模糊层次理论的路网匹配模型，该模型通过融合多个评价指标进行构建，并对其指标权重进行优化，采用该模型对路网的一致性进行评价. 以长株潭城市群道路网为实验对象进行实验验证. 实验结果表明：提出的匹配模型能够有效改善道路网匹配精度，且具有较好的稳定性和可靠性. 道路匹配的准确率和召回率分别达到了92.9%和86.2%，比现有的典型匹配方法的86.7%和83.8%均有了显著提高. 研究成果为道路网匹配与评价提供一种新的更为有效之方法.Abstract: With the acceleration of the infrastructure construction of national urban agglomeration, the demand for the current situation of urban road network data has increased sharply. Aiming at the instability of the current road network matching algorithm with location and length as the main parameters, as well as the uncertainty and poor reliability of the analysis results, a new road network matching model based on fuzzy hierarchy theory is proposed. The model is constructed by integrating multiple evaluation indexes, and the index weight is optimized. The model is used to evaluate the consistency of the road network. The road network of Changsha Zhuzhou Xiangtan Urban Agglomeration is taken as the experimental object for experimental verification. The experimental results show that the matching model proposed in this paper can effectively improve the matching accuracy of road network, and has good stability and reliability. The accuracy and recall of road matching have reached 92.9% and 86.2% respectively, which is significantly higher than 86.7% and 83.8% of the existing typical matching methods. The research results provide a new and more effective method for road network matching and evaluation.
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Key words:
- urban group /
- road network /
- matching model /
- Fuzzy hierarchy process /
- Chang-Zhu-Tan city group /
- geography
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图 2 道路Stroke闭合面积相似性度量
a. Stroke1组成的闭合区域；b. Stroke2组成的闭合区域；c. 重叠之后的图形
Fig. 2. Similarity measure of closed area of road Stroke
图 5 道路网数据层次化处理
a. 规划数据道路网展示；b. 现状数据道路网展示；c. 规划数据道路网Stroke分级结果；d. 现状数据道路网Stroke分级结果；e. 规划数据与现状数据叠加结果
Fig. 5. Hierarchical data processing of road network data
表 1 优先判断矩阵表
Table 1. Priority judgment matrix table
A B1 B2 B3 B4 B1 0.5 1 1 1 B2 0 0.5 1 1 B3 0 0 0.5 1 B4 0 0 0 0.5 
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表 2 模糊一致阵变换结果
Table 2. Results of fuzzy uniform matrix transformation
A B1 B2 B3 B4 B1 0.500 0.625 0.750 0.875 B2 0.375 0.500 0.625 0.750 B3 0.250 0.375 0.500 0.625 B4 0.125 0.250 0.375 0.500
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表 3 初始道路网数据要素统计结果
Table 3. Statistical results of data elements of initial road network
初始数据 预处理后数据 弧段数量 结点数量 弧段数量 结点数量 规划数据 2 856 3 358 2 367 2 614 现状数据 3 267 3 754 2 849 3 243
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表 4 道路网数据Stroke处理结果
Table 4. Stroke processing results of road network data
预处理后数据 Stroke构建结果 弧段数量 结点数量 Stroke数量 规划数据 2 367 2 614 507 现状数据 2 849 3 243 538
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表 5 匹配结果统计表
Table 5. Quantitative statistical analysis of matching results
统计指标 正确匹配 错误匹配 漏匹配 匹配正确率（%） 匹配召回率（%） 结点-弧段匹配数量 4 106 630 794 86.7 83.8 模糊层次匹配数量 421 32 67 92.9 86.2
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