Abstract
A road network represents a set of road objects in a geographic area and their interconnections, and it is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics may coexist. In this paper, we present an area partitioning and subgraph growing (APSG) approach to the conflation of two road networks with a large difference in the level of details and representation rules. Our area partitioning (AP) scheme partitions the geographic area using the Network Voronoi Area Diagram (NVAD) of the low-detailed road network. Next, a subgraph of the high-detailed road network corresponding to a complex intersection is extracted and aggregated into a supernode so that high precision can be achieved via 1:1 road object matching. For the unmatched road objects due to missing road objects and different representation rules, we also propose a subgraph growing (SG) scheme that sequentially inserts a new road object while keeping the consistency of its connectivity to the matched road objects by the AP scheme. From the numerical results at Yeouido, Seoul, Korea, we show that our APSG scheme can achieve an outstanding matching performance in terms of the precision, recall, and F1-score.