A Linkage Matching Method for Road Networks Considering the Similarity of Upper and Lower Spatial Relation

doi:10.11947/j.AGCS.2016.20160062

Abstract

Abstract: Existing road network matching methods mostly use the characteristics of the road's own nodes and arcs to carry on the matching process, while less attention is focused on the importance of the road neighborhood elements in the road network matching, thus affecting further improvement of the matching efficiency and accuracy. In response to these problems, a linkage matching method for road network considering the similarity of upper and lower spatial relation is proposed. The linkage matching imitates the human thinking process of searching for target objects by the signal features and spatial correlation when reading maps, regarding matching as a reasoning process of goal feature searching and information association transmitting. Firstly, classify the complex road network by using Stroke technology. Secondly, establish the road network linkage matching model based on road skeleton relation tree. Finally, select the high-level road in the classifying results of the source data as the reference road to start matching, calculate the road between the upper and lower levels of the spatial relationship similarity, and through a step-by-step iteration, make the matching information transmit in the road network linkage matching model thus to obtain the final matching results. Experiment shows that the mentioned algorithm can narrow the search range of the data to be matched, effectively improving the match efficiency and accuracy, especially applicable to the data with large non systematic geometric location deviation.

Key words: linkage matching, spatial relations, Stroke technology, information transfer

CLC Number:

P208

LIU Chuang, QIAN Haizhong, WANG Xiao, HE Haiwei, CHEN Jingnan. A Linkage Matching Method for Road Networks Considering the Similarity of Upper and Lower Spatial Relation[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(11): 1371-1383.

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