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.
翻译:在许多以空间轨迹为基础的应用中,有必要将原始轨迹数据点绘制成数字地图中的道路网络,这通常被称为地图比对过程。虽然以往的多数地图比对方法都侧重于使用基于规则的算法来处理地图比对问题,但在本文中,我们从数据驱动的角度来考虑地图比对任务,提出一个深层次的基于学习的地图比对模型模型模型。我们建立一个基于变换器的地图比对模型和转移学习方法。我们生成轨迹数据,对变换器模型进行预先培训,然后用有限的地面比对数据微调模型,以尽量减少模型开发成本并减少实际到虚拟差距。我们用三个指标(常态模拟距离、F-score和BLEU)来评价模型的性能。结果显示,拟议的模型比现有模型更符合现有模型。此外,我们利用变换器的注意重来绘制地图比对齐进程,并找到模型如何正确匹配路段。