An improved probabilistic relaxation method for matching multi-scale road networks

Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other. The quality of the merge or update depends greatly on the matching accuracy of the two road networks. We propose an improved probabilistic relaxation method, considering both local and global optimizations for matching multi-scale of road networks. The aim is to achieve local optimization, as well as to address the identification of the M:N matching pattern by means of inserting virtual nodes to achieve global optimization effects. Then, by adding two attribute-related evaluation indicators, we developed four evaluation indicators to evaluate the matching accuracy, considering both geographic and attribute information. This paper also provides instructions on how to identify the proper buffer threshold during matching procedures. Extensive experiments were conducted to compare the proposed method with the traditional approach. The results indicate that: (1) the overall matching accuracy of each evaluation indicator exceeds 90%; (2) the overall matching accuracy increases by 6–12% after an M:N matching pattern is added, and by 4–6% following the addition of topology indicators; and (3) the proper buffer threshold is about twice the average value of the closest distance from all nodes.