利用概率松弛法的城市路网自动匹配

多源空间数据匹配是空间数据集成与互操作，变化检测与数据更新的重要前提。路网数据匹配在导航、智能交通和基于位置服务等领域具有重要的研究意义和实用价值。本文提出一种基于概率松弛方法的城市路网自动匹配方法，该方法首先通过路段间几何差异性估算候选路段的初始概率，然后根据邻接候选匹配路段的兼容性不断更新原概率矩阵直到收敛于某一极小值。最后基于收敛的概率矩阵计算各候选路段的结构相似性，并通过设定相应的规则选取和提炼1: 1, 1: M和M: N匹配对。实验选取中国武汉，瑞士苏黎世地区的OpenStreetMap数据与导航数据进行匹配算法的验证。结果表明：本文算法对非刚性偏差较大的路网数据能达到较高精度，不存在匹配方向性问题，且能够识别1: 0, 1: M和M: N匹配。

Automated Matching Urban Road Networks Using Probabilistic Relaxation

Multiple spatial data matching is a crucial prerequisite for data integration and interoperability, change detection and data updating. Road network matching is of great theoretical and practical significances in Navigation, Intelligent Transportation System and Location-Based Services. The paper proposes a probabilistic relaxation approach for matching urban road networks. The proposed method starts with an initial probabilistic matrix according to the geometric dissimilarities, and then integrates the effects of neighbouring roads to update the old probabilistic matrix until it is convergent to a specified small value. Finally, on the basis of the convergent probabilistic matrix, the structural similarity of each candidate pair is calculated and the corresponding rules are defined to select and refine 1: 1, 1: M and M: N matches. Two experiments of matching between Open Street Map network data and professional road network data in Wuhan and Zurich show that our method achieves a robust matching precision for large non-rigid deviation, is independent of matching direction, and successfully matches 1: 0 (Null), 1: 1 and M: N pairs.