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Identifying homonymous road objects is a crucial prerequisite to the integration, updating and fusion of road data. Road networks matching is of great theoretical research value and practical significance in aspect of intelligent transportation system and location-based Service. This paper proposed an optimization algorithm for multi-characteristics road network matching. Designed from shape, distance and semantics aspects, three similarity characteristics-shape differences based on area accumulated, mixed median Hausdorff distance and distance with global weighted attributes, described candidate corresponding pairs more accurately. Then, the matching regression model could be then constructed by training the similarity samples set through SVM algorithm. Finally, the constructed model can be used to predict whether the road matching pairs were matched. A great number of experiments show that the algorithm achieves a robust matching precision and recall even for road networks data with apparent non-rigid deviation. And the proposed method can be effectively applied for road networks matching with multiple matching relationship.
