A Suitable-Area Selection Method for Road Vector Map Matching Based on a Hypergraph-Enhanced Graph Attention Network

Suitable-area selection reduces candidate-search costs and mismatch risk in road vector map matching. Existing image-based assessments primarily characterize grayscale, texture, and edge saliency, whereas the suitability of road vector maps depends on road continuity, intersection organization, and topological uniqueness. This study proposes a high-confidence suitable-area selection framework based on a Hypergraph-Enhanced Graph Attention Network (HG-GAT). A road-network suitability dataset is constructed using offline matching results. Candidate regions are represented as road graphs, with semantic hyperedges encoding road chains, intersection stars, and key-node neighborhoods. Hypergraph propagation and graph attention jointly learn high-order structural relations and pairwise road connections, while dual-channel readout integrates regional organization and salient local structures. On the Wuhan test data, HG-GAT achieves an accuracy of 0.8074, an F1-score of 0.8309, and a Precision@10% of 0.8980, the highest values for these metrics among the compared methods. It retains high-confidence candidates with a recall of 0.9467 while reducing false selections relative to the image-based and ordinary graph-learning baselines. The results support the value of semantic high-order topology for distinguishing structurally effective road regions and prioritizing candidates for subsequent matching.