mlsanityData to 7 Oct 2026

SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory

arXiv 2308.07555 ↗First posted 15 Aug 2023cs.CV cs.CYcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

12 of 28 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2020A Survey on Visual Transformercited by 653
  6. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  7. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  8. 2021Multimodal Motion Prediction with Stacked Transformerscited by 207
  9. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  10. 2017Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Networkcited by 56
  11. 2016T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Predictioncited by 9
  12. 2021Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenescited by 5

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Citing papers per year: 2024: 4.

4 papers.

  1. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  2. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  3. 2024A Unified Framework for Next-Gen Urban Forecasting via LLM-driven Dependency Retrieval and GeoTransformercited by 0
  4. 2024Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)cited by 0