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.

翻译：准确预测出租车轨迹的目的地可为智能位置服务带来诸多益处。实现这一预测的一种潜在方法是将出租车轨迹转换为二维网格，并应用计算机视觉技术。尽管Swin Transformer是一种创新的计算机视觉架构，在视觉下游任务中展现出显著成效，但它在解决现实轨迹问题中的应用尚不普遍。本文提出了一种简化的Swin Transformer（SST）结构，该结构未采用传统Swin Transformer中的移位窗口思想，因为轨迹数据具有天然连续性。基于真实轨迹数据的综合实验表明，与现有最优方法相比，SST能够实现更高的预测精度。