  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 paper, 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 ground-truth data to minimize the model
development cost and reduce the real-to-virtual gap. Three metrics (Average
Hamming Distance, F-score, and BLEU) at two levels (point and segment level)
are used to evaluate the model performance. The results indicate that the
proposed model outperforms existing models. Furthermore, we use the attention
weights of the Transformer to plot the map-matching process and find how the
model matches the road segments correctly.
