/deep-eta-murat

Implementation of "Multi-task Representation Learning for Travel Time Estimation"

MIT LicenseMIT

Multi-task Representation Learning for Travel Time Estimation

This is a PyTorch implementation of MURAT in the following paper (code is being added ...):
Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye and Yan Liu, Multi-task Representation Learning for Travel Time Estimation, KDD 2018.

Origin-Destination Travel Time Estimation

Given an origin, a destination and a departure time, the model want to estimate the time of arrival.

Model Architecture

Multi-task Representation Learning for Travel Time Estimation

Poster (Click to see the PDF)

Requirements

  • scipy>=0.19.0
  • numpy>=1.12.1
  • pandas>=0.19.2
  • pytorch>=0.3.0
  • pyaml

Dependency can be installed using the following command:

pip install -r requirements.txt

More details are being added ...

Citation

If you find this repository useful in your research, please cite the following paper:

@inproceedings{li2018kdd_deep_eta,
  title={Multi-task Representation Learning for Travel Time Estimation},
  author={Li, Yaguang and Fu, Kun and Wang, Zheng and Shahabi, Cyrus and Ye, Jieping and Liu, Yan},
  booktitle={International Conference on Knowledge Discovery and Data Mining (KDD '18)},
  year={2018}
}