SST: A Simplified Swin Transformer-based Model for Taxi Destination
  Prediction based on Existing Trajectory
Zepu Wang Yifei Sun Zhiyu Lei Xincheng Zhu Peng Sun
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.