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.