ST2Vec

This is our Pytorch implementation for the paper:

Ziquan Fang, Yuntao Du, Xinjun Zhu, Danlei Hu, Lu Chen, Yunjun Gao and Christian S. Jensen. (2022). Spatio-Temporal Trajectory Similarity Learning in Road Networks. Paper in ACM DL or Paper in arXiv. In KDD'22, Washington DC, USA, August 14-18, 2022.

Introduction

ST2Vec is a representation learning based solution that considers fine-grained spatial and temporal relations between trajectories to enable spatio-temporal similarity computation in road networks.

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{ST2Vec22,
  author    = {Ziquan Fang and
               Yuntao Du and
               Xinjun Zhu and
               Danlei Hu and 
               Lu Chen and 
               Yunjun Gao and
               Christian S. Jensen},
  title     = {Spatio-Temporal Trajectory Similarity Learning in Road Networks},
  booktitle = {{KDD}},
  pages = {347–356},
  year      = {2022}
}

Requirements

Ubuntu OS
Python >= 3.5 (Anaconda3 is recommended)
PyTorch 1.4+
A Nvidia GPU with cuda 10.2+

Datasets

Trajectory dataset (TDrive) and Rome are an open source data set
We provided the road network data and map-matching result data

Reproducibility & Training

Data preprocessing (Time embedding and node embedding)
```
python preprocess.py
```

Ground truth generating (It will take a while...)

python spatial_similarity.py
python temporal_similarity.py