The embedding model for categorical data analysis in transport.

Introduction

This is an open-source (MIT) Pytorch based code repository (feature embedding) for the following paper:

"Wang, B., Shaaban, K. and Kim, I., 2019. Revealing the hidden features in traffic prediction via entity embedding. Personal and Ubiquitous Computing, pp.1-11."

The feature embedding is designed to represent discreate (or categorical) variables in traffic forecasting tasks. More information can be found at http://resuly.me/2020/02/18/embedding-in-transport/

Usage

The main code located in the model folder and the visualization works can be found in visualization.

To run the embedding model, you will need to install PyTorch environment and run the following command:

python train.py --model EM

See the results in experiments/EM

Citation

If you think this is helpful to your research, please consider citing our work:

@article{wang2019revealing,
  title={Revealing the hidden features in traffic prediction via entity embedding},
  author={Wang, Bo and Shaaban, Khaled and Kim, Inhi},
  journal={Personal and Ubiquitous Computing},
  pages={1--11},
  year={2019},
  publisher={Springer}
}