Acta Geodaetica et Cartographica Sinica >

2016 , Vol. 45 >Issue 1: 103 - 111

DOI: https://doi.org/10.11947/j.AGCS.2016.20140588

Matching Method for Road Networks Considering the Similarity of the Neighborhood Habitation Cluster

  • WANG Xiao ,
  • QIAN Haizhong ,
  • HE Haiwei ,
  • CHEN Jingnan ,
  • HU Huiming
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  • Institute of Geospatial Information, Information Engineering University, Zhengzhou 450052, China

Received date: 2014-11-12

  Revised date: 2015-06-17

  Online published: 2016-01-28

Supported by

The National Natural Science Foundation of China (Nos. 41171305;41171354;40701157);Master's Degree Dissertation Innovation Fundation of Geospatial Information Institute, Information Engineering University(No.S201403)

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Abstract

The existing matching methods for the multi-source road data of the same scale mainly consider the characteristics of the road itself, while the effect of the neighborhood features on matching process is generally ignored, which may restrict the further improvement of the matching results. This restriction can be more obvious for the matching data in which the location or rotation differences still exist after the system error rectification. A road network matching method that takes the similarity of the roads' neighborhood habitation cluster into consideration is proposed, which draws on the experience of the human spatial cognitive characteristics for the unfamiliar environment. Firstly, the neighborhood habitation cluster of the road is extracted by the urban skeleton line network; Then by calculating the spatial relation similarity and geometry characteristic similarity of the neighborhood habitation cluster, the matching results can be obtained. The advantage of this method is that for the road data which have obvious location or rotation differences, the similarity of their neighborhood habitation clusters can be treated as a proper matching index. Actually, roads' neighborhood habitation cluster can be a constraint of the road matching process and enhance its robustness. The tests and comparison analysis indicate that this method can solve the matching problems of the road data which still have obvious location or rotation differences after system error rectification and improve the matching correctness.

Key words： multi-source road networks matching; urban skeleton-line network; neighborhood environment; habitation cluster; area cluster similarity calculation

Cite this article

WANG Xiao , QIAN Haizhong , HE Haiwei , CHEN Jingnan , HU Huiming . Matching Method for Road Networks Considering the Similarity of the Neighborhood Habitation Cluster[J]. Acta Geodaetica et Cartographica Sinica, 2016 , 45(1) : 103 -111 . DOI: 10.11947/j.AGCS.2016.20140588

References

[1] 郝燕玲, 唐文静, 赵玉新, 等. 基于空间相似性的面实体匹配算法研究[J]. 测绘学报, 2008, 37(4): 501-506. HAO Yanling, TANG Wenjing, ZHAO Yuxin, et al. Areal Feature Matching Algorithm Based on Spatial Similarity[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(4): 501-506.
[2] QUDDUS M A, OCHIENG W Y, NOLAND R B. Map Matching Algorithms for Intelligent Transport Systems Application[C]//Proceedings of the 13th World Congress on Intelligent Transport Systems and Services. London: TRID, 2006.
[3] WALTER V, FRITSH D. Matching Spatial Data sets: A Statical Approach[J]. International Journal of Geographical Information Science, 1999, 13(5): 445-473.
[4] ZHANG Meng, MENG Liqiu. An Iterative Road-Matching Approach for the Integration of Postal Data[J]. Computers, Environment and Urban Systems, 2007, 31(5): 597-615.
[5] 童小华, 邓愫愫, 史文中. 基于概率的地图实体匹配方法[J]. 测绘学报, 2007, 36(2): 210-217. TONG Xiaohua, DENG Susu, SHI Wenzhong. A Probabilistic Theory-based Matching Method[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 210-217.
[6] 胡云岗, 陈军, 赵仁亮, 等. 地图数据缩编更新中道路数据匹配方法[J]. 武汉大学学报(信息科学版), 2010, 35(4): 451-456. HU Yungang, CHEN Jun, ZHAO Renliang, et al. Matching of Roads under Different Scales for Updating Map Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 451-456.
[7] 赵东保, 盛业华. 全局寻优的矢量道路网自动匹配方法研究[J]. 测绘学报, 2010, 39(4): 416-421. ZHAO Dongbao, SHENG Yehua. Research on Automatic Matching of Vector Road Networks Based on Global Optimization[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4): 416-421.
[8] 栾学晨, 杨必胜, 李秋萍. 基于结构模式的道路网节点匹配方法[J]. 测绘学报, 2013, 42(4): 608-614. LUAN Xuechen, YANG Bisheng, LI Qiuping. Pattern-basede Node Matching Approach for Road Network[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4): 608-614.
[9] 张云菲, 杨必胜, 栾学晨. 利用概率松弛法的城市路网自动匹配[J]. 测绘学报, 2012, 41(6): 933-939. ZHANG Yunfei, YANG Bisheng, LUAN Xuechen. Automated Matching Urban Road Networks Using Probabilistic Relaxation[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6): 933-939.
[10] 陈玉敏, 龚健雅, 史文中. 多尺度道路网的距离匹配算法研究[J]. 测绘学报, 2007, 36(1): 84-90. CHEN Yumin, GONG Jianya, SHI Wenzhong. A Distance-based Matching Algorithm for Multi-scale Road Networks[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1): 84-90.
[11] 刘志勇. 城市地图数据库合并中的面实体匹配方法研究[D]. 南京: 河海大学, 2006. LIU Zhiyong. Research on Areal Feature Matching Algorithm in the Conflation of Urban Geographic Databases[D]. Nanjing: Hohai University, 2006.
[12] 焦洋洋, 王卉, 翟仁健. 顾及邻域相似性的面要素匹配方法[J]. 辽宁工程技术大学学报(自然科学版), 2013, 32(7): 947-952. JIAO Yangyang, WANG Hui, ZHAI Renjian. Matching Method for Areal Feature Considering Context-dependent Similarity[J]. Journal of Liaoning Technical University: Natural Science, 2013, 32(7): 947-952.
[13] 吴建华. 顾及环境相似的多特征组合实体匹配方法[J]. 地理与地理信息科学, 2010, 26(4): 1-6. WU Jianhua. Entity Matching Methods Based on Combining Multi-Similarity-Characteristics Considering Environment Similarity[J]. Geography and Geo-Information Science, 2010, 26(4): 1-6.
[14] 钱海忠, 武芳, 朱鲲鹏, 等. 一种基于降维技术的街区综合方法[J]. 测绘学报, 2007, 36(1): 102-107. QIAN Haizhong, WU Fang, ZHU Kunpeng, et al. A Generalization Method of Street Block Based on Dimension-reducing Technique[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1): 102-107.
[15] 刘涛. 空间群(组)目标相似关系及计算模型研究[D]. 武汉: 武汉大学, 2011. LIU Tao. Similarity of Spatial Group Objects[D]. Wuhan: Wuhan University, 2011.
[16] 刘涛, 闫浩文. 空间面群目标几何相似度计算模型[J]. 地球信息科学学报, 2013, 15(5): 635-642. LIU Tao, YAN Haowen. Geometry Similarity Assessment Model of Spatial Polygon Groups[J]. Journal of Geo-Information Science, 2013, 15(5): 635-642.
[17] 江浩, 褚衍东, 闫浩文, 等. 多尺度地理空间点群目标相似关系的计算研究[J]. 地理与地理信息科学, 2009, 25(6): 1-4. JIANG Hao, CHU Yandong, YAN Haowen, et al. Study on Computation of Similarity Relationships of Multi-scale Point Objects[J]. Geography and Geo-Information Science, 2009, 25(6): 1-4.
[18] 刘涛, 杜清运, 闫浩文. 空间点群目标相似度计算[J]. 武汉大学学报(信息科学版), 2011, 36(10): 1149-1153. LIU Tao, DU Qingyun, YAN Haowen. Spatial Similarity Assessment of Point Clusters[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1149-1153.
[19] 刘涛, 杨树文, 李轶鲲, 等. 空间线群目标方向相似度计算模型[J]. 测绘科学, 2013, 38(1): 156-159. LIU Tao, YANG Shuwen, LI Yikun, et al. Direction Similarity Calculation Model of Spatial Line Groups[J]. Science of Surveying and Mapping, 2013, 38(1): 156-159.
[20] EGENHOFER M J, MARK D M. Naive Geography[C]//FRANK A, KUHN W. Spatial Information Theory: A Theoretical Basis for GIS, International Conference COSIT' 95. Semmering, Austria: Springer-Verlag, 1995, 988: 1-15.
[21] 艾廷华, 刘耀林. 保持空间分布特征的点群化简方法[J]. 测绘学报, 2002, 31(2): 175-181. AI Tinghua, LIU Yaolin. A Method of Point Cluster Simplification with Spatial Distribution Properties Preserved[J]. Acta Geodaetica et Cartographica Sinica, 2002, 31(2): 175-181.
[22] 安晓亚, 孙群, 肖强, 等. 一种形状多级描述方法及在多尺度空间数据几何相似性度量中的应用[J]. 测绘学报, 2011, 40(4): 495-501. AN Xiaoya, SUN Qun, XIAO Qiang, et al. A Shape Multilevel Description Method and Application in Measuring Geometry Similarity of Multi-scale Spatial Data[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4): 495-501.
[23] BRUNS H T, EGENHOFER M J. Similarity of Spatial Scenes[C]//KRAAK J M, MOLENAAR M. Proceedings of the 7th International Symposium on Spatial Data Handling. Delft: Taylor & Francis, 1996: 173-184.
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