Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

Related tags

Deep Learning CGAN-DF
Overview

Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

This code implements the paper, Kim et al. (2021). Imputing Qualitative Attributes for Trip Chains Extracted from Smart Card Data Using a Conditional Generative Adversarial Network. Transportation Research Part C. Under Review.

Overview

This model aims to estimate the qualitative attributes of large-scale passively collected data (smart card data) using small-scale travel survey data, based on data fusion. The CGAN trains probability distribution of qualitative attributes given trip-chain attributes by mimicking the small-scale survey data..

Getting Started

Dependencies

  • Python 3.6.10
  • Tensorflow 2.4.1, Keras 2.4.3

Components

Dataset

  • 'Data' only contains pertubated and sampled smart card and travel survey data due to limited permission.
  • train/test_incomplete data indicate the smart card containing trip-chain attributes
  • train/test_complete data indicate the travel survey containing trip-chain and qualitative attributes
  • Other data is obtained from the DataPreprocessing.ipynb
DataPreprocessing.ipynb
  • DataPreprocessing transforms the trip-chain attributes into sequential ndarray to use for Tensorflow
  • Detailed descriptions are provided in the notebook files.
2D-Transformer.ipynb
  • Step-by-step implementation of CGAN for mobility data fusion is provided
  • Class for Transformer, 1D-Positional, and 2D-Locational encoding are defined
  • The code include all parts in the paper: Model structure (2D-Transformer), Model training (Conditional WGAN-GP), Evaluation (Fidelity and Diversity), and Visualization
  • Pretained model with full training data is provided in the 'Py_generator'
BERT_Embed.ipynb
  • BERT transforms categorical qualitative attributes into numeric one to use for calculating precision and recall
  • Pretained model is also provided ('MLM_Embed_indiv.h5')

Notice

  • Full paper will be provided after the peer-review process
  • Detail logic behind the code is described in the full paper

Authors

@Eui-Jin Kim

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

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