Transformer-based map-matching model with limited labeled data using transfer-learning approach

Abstract

In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process. While most previous map-matching methods have focused on using rule-based algorithms to deal with the map-matching problems, in this thesis, we consider the map-matching task from the data-driven perspective, proposing a deep learning-based map-matching model. We build a Transformer-based map-matching model with a transfer learning approach. We generate trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of labeled data to minimize the model development cost and reduce the real-to-virtual gaps. Three metrics (Average Hamming Distance, F-score, and BLEU) at two levels (point and segment level) are used to evaluate the model performance. The model is tested with real-world datasets, and the results show that the proposed map-matching model outperforms other existing map-matching models. We also analyze the matching mechanisms of the Transformer in the map-matching process, which helps to interpret the input data internal correlation and external relation between input data and matching results. In addition, the proposed model shows the possibility of using generated trajectories to solve the map-matching problems in the limited labeled data environment