A two-stage Map-matching Method for Sparse Trajectories with Small-scale Ground Truth Data (S)

Trajectory data often suffer from sparsity and a lack of ground truth data, presenting challenges for map-matching.Traditional map-matching approaches struggle with sparse data, and deep learning methods are hindered by data scarcity.To address these issues, we propose a two-stage map-matching method.In the first stage, we use synthetic trajectory data to pre-train a deep learning model, overcoming data scarcity.We introduce a token mixing scheme during pre-training to clarify the interrelations between GPS-based and segment-based trajectory data.In the second stage, we fine-tune the model with limited ground truth data using a Transformer architecture.Empirical evaluations show that our model achieves 3.69%, 2.21% average precision and recall improvements over the best baseline model with various sparseness.