derekwtian/rntrajrec

Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer (ICDE'23)

RNTrajRec

About

Source code of the ICDE'23: RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

Requirements

• Python==3.6
• pytorch==1.8.0
• rtree==0.9.4
• GDAL==2.3.3
• networkx==2.3
• dgl=0.8.0.post2
• seaborn=0.11.2
• chinese-calendar=1.6.1

Data format

OSM map format

Map from OSM that contains: edgeOSM.txt nodeOSM.txt wayTypeOSM.txt.

Train data format

Train data has the following format:

____ ROOT
  |____ train
    |____ train_input.txt
    |____ train_output.txt
  |____ valid
    |____ valid_input.txt
    |____ valid_output.txt
  |____ test
    |____ test_input.txt
    |____ test_output.txt

Note that:

• {train/valid/test}_input.txt contains raw GPS trajectory, {train/valid/test}_output.txt contains map-matched trajectory.
• The sample rate of input and output file for train and valid dataset in both raw GPS trajectory and map-matched trajectory need to be the same, as the downsampling process in done while obtaining training item.
• The sample rate of test input and output file is different, i.e. test_input.txt contain low-sample raw GPS trajectories and test_output.txt contain high-sample map-matched trajectories.
• We provide a toy dataset in Porto under ./data/Porto/ and OSM map for Porto under ./data/roadnet/.

Training and Testing

• For training & testing with $\epsilon_\tau=\epsilon_\rho*8$ in Porto dataset, run the following command:

python -u multi_main.py --city Porto --keep_ratio 0.125 --pro_features_flag \
      --tandem_fea_flag --decay_flag

• For training & testing with $\epsilon_\tau=\epsilon_\rho*16$ in Porto dataset, run the following command:

python -u multi_main.py --city Porto --keep_ratio 0.0625 --pro_features_flag \
      --tandem_fea_flag --decay_flag

Citations

If you find this repo useful and would like to cite it, citing our paper as the following will be really appropriate:

@article{chen2022rntrajrec,
  title={RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer},
  author={Chen, Yuqi and Zhang, Hanyuan and Sun, Weiwei and Zheng, Baihua},
  journal={arXiv preprint arXiv:2211.13234},
  year={2022}
}