A two-stage Map-matching Method for Sparse Trajectories with Small-scale Ground Truth Data (S)
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Junjun Si, Yang Xiang, Li Li, Bo Tu
Institution: Communication University of China
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Texas Space Grant Consortium
OpenAlex ID: W4406255596