Spatial Hierarchical Meta-Learning for Single-Point Map Matching

Inferring the actual road segment purely based on one positioning point, known as single-point map matching (SMM), is vital for many urban applications, e.g., ride-hailing and geo-tagging. However, it is challenging due to inherent positioning errors and extrinsic heterogeneous environments. Existing methods either overlook the heterogeneity of different regions, or do not exploit the commonality of different matching tasks. In this paper, we treat each region as an individual SMM task to tackle the heterogeneity, and propose Spatial Hierarchical Meta-Learning for SMM (SHSMM) to learn the shared knowledge across tasks. SHSMM is equipped with a Dual-view Map Matcher to perform the matching, which can perceive the knowledge of road segments globally. To learn the task-specific model parameters, SHSMM modulates initial parameters and scales the local update learning rate based on hierarchical geographical and semantic knowledge about spatial tasks. A local update learning rate scheduling strategy is further proposed to facilitate the meta-training. Extensive experiments as well as case studies based on two real-world datasets demonstrate the effectiveness of the proposed method.