Accurately predicting the destination of taxi trajectories can have various
benefits for intelligent location-based services. One potential method to
accomplish this prediction is by converting the taxi trajectory into a
two-dimensional grid and using computer vision techniques. While the Swin
Transformer is an innovative computer vision architecture with demonstrated
success in vision downstream tasks, it is not commonly used to solve real-world
trajectory problems. In this paper, we propose a simplified Swin Transformer
(SST) structure that does not use the shifted window idea in the traditional
Swin Transformer, as trajectory data is consecutive in nature. Our
comprehensive experiments, based on real trajectory data, demonstrate that SST
can achieve higher accuracy compared to state-of-the-art methods.

通过将出租车轨迹转化为二维网格并使用计算机视觉技术，本研究提出了一种简化的 Swin Transformer 结构 (SST) 用于准确预测出租车轨迹的目的地，并通过基于真实轨迹数据的综合实验表明，SST 相较于现有方法能够实现更高的准确性。