全局与局部寻优相结合的道路网匹配方法

针对面向道路网匹配的概率松弛法约束性指标单一且无法识别M:N匹配模式的不足，从兼顾全局和局部匹配最优的角度出发，提出了从局部角度顾及几何约束和拓扑约束，从全局角度完善M:N匹配模式的改进算法，设计并实现了不同匹配模式下的匹配策略。测试结果表明，该方法的整体匹配精度和召回率提高了7%~14%，均达到90%以上；空间与属性匹配度评价指标提高了3%~7%；可将待匹配路网中最邻近结点平均距离的两倍值作为缓冲区阈值设定的参考依据，从而验证了该方法的可行性与可靠性。

Matching Road Networks Based on Combination of Global and Local Optimization

To address the problems that the traditional probabilistic relaxation method only adopted geometric constraints as one of road matching criterions and could not respond to M:N matching pattern, we propose an improved probabilistic relaxation method from the combined views of local optimization and global one, integrating geometric indicators with topology ones to achieve an effect with local optimization, as well as identifying M:N matching pattern by inserting virtual nodes to achieve a globally optimal effect. Then we design the matching strategies and corresponding implement algorisms for different matching patterns. The case test showed that the overall matching accuracy of each evaluation indictor reached over 90%, increasing by 7%-14%; the evaluation indicators on both spatial and attribute properties increased by 3%-7%; the proper buffer threshold can be defined as twice the average value of the closest distances from all nodes in the candidate matching dataset.