xunhs / st2vec

Source code for Spatio-Temporal Trajectory Similarity Learning in Road Networks. KDD 2022.

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

1. Data preprocessing (Time embedding and node embedding)

   python preprocess.py

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

   python spatial_similarity.py
   python temporal_similarity.py

3. Triplets generating

   python data_utils.py

4. Training

   python main.py