Privacy Preserving Framework to anonymize user trajectories contained in a population of users. It allows for researchers and engineers to plug-in any dataset & model into our current system.

Processing GPS location time series data with a Trajectory Generative Adversarial Network to generate synthetic data.

This is a TensorFlow 2 project and if you plan to train models on an NVIDIA GPU we recommend using conda to install the dependencies because each version of TensorFlow only works with a specific version of CUDA (see TensorFlow docs for tested configurations) and conda can install isolated CUDA versions in its environments to prevent conflicts.

conda env create -f environment.yml
conda activate

1. Add two environment variables to a .env file in this (root) directory: xxx_INPUT_DIR which is the absolute directory path of the raw input dataset on your system, and xxx_INPUT_FILE, which is a file location where you want preprocessed data to be saved, i.e. a CSV file. Replace the xxx with a 3-letter "nickname" for your dataset.
2. Create a new .py file in src/datasets/ for your dataset.
3. Write a class that subclasses Dataset (from src/datasets/base.py) and implements a preprocess() method that reads in the raw data and returns a pandas.DataFrame.
4. Import your class into src/datasets/init.py and add the class name to the DATASETS list.

1. Create a new .py file in src/models/ and write a class that inherits from TrajectoryModel (in src/models/base.py) and implements at least train, predict, save and restore abstract methods. If it's a supervised model (like MARC) then you'll also want to add an evaluate method to get metrics on the test set.
2. Import your model class into src/models/init.py and add the class name to the MODELS list.

Use the CLI script's train command:

$ python mobility_cli.py
Usage: mobility_cli.py [OPTIONS] COMMAND [ARGS]...

  Command line interface for the mobility learning framework.

Options:
  --help  Show this message and exit.

Commands:
  evaluate  Use SAVED_MODEL to predict the labels of DATASET.
  predict   Use trained MODEL saved in SAVED_PATH to make predictions based...
  train     Train MODEL on DATASET stored in DATASET_PATH for EPOCHS.

$ python mobility_cli.py train --help
Usage: mobility_cli.py train [OPTIONS] [LSTMTrajGAN|MARC] [MDCLausanne|GeoLife
                             Beijing|FourSquareNYC|PrivamovLyon] EPOCHS

  Train MODEL on DATASET stored in DATASET_PATH for EPOCHS.

Options:
  --help  Show this message and exit.

$ python mobility_cli.py train LSTMTrajGAN GeoLifeBeijing 200

Use the CLI script's predict command:

$ python mobility_cli.py predict --help
Usage: mobility_cli.py predict [OPTIONS] [LSTMTrajGAN|MARC] SAVED_PATH [MDCLau
                               sanne|GeoLifeBeijing|FourSquareNYC|PrivamovLyon
                               ] OUTPUT_PATH

  Use trained MODEL saved in SAVED_PATH to make predictions based on DATASET
  and write to OUTPUT_PATH as CSV.

Options:
  --help  Show this message and exit.

$ python mobility_cli.py predict LSTMTrajGAN LSTMTrajGAN experiments/LSTMTrajGAN_GeoLifeBeijing/2021-07-24T23:55:37/saved_model/ outputs/LSTMTrajGAN_GeoLifeBeijing_predictions.csv

Use the CLI script's evaluate command:

$ python mobility_cli.py evaluate --help
Usage: mobility_cli.py evaluate [OPTIONS] [LSTMTrajGAN|MARC] SAVED_PATH [MDCLa
                                usanne|GeoLifeBeijing|FourSquareNYC|PrivamovLy
                                on]

  Use SAVED_MODEL to predict the labels of DATASET.

Options:
  --help  Show this message and exit.

• Port LSTM-TrajGAN to TensorFlow 2 so it can be run in the same environment
• Preprocessing code for MDC data so it can be fed into LSTM-TrajGAN
• Port MARC reidentifier model to TF2
• Preprocessing code for GeoLife data so it can be fed into LSTM-TrajGAN
• Preprocessing code for Privamov data so it can be fed into LSTM-TrajGAN
• Post-processing code to output LSTM-TrajGAN generated trajectories to CSV
• Train LSTM-TrajGAN on MDC Lausanne dataset
• Train LSTM-TrajGAN on Foursquare NYC dataset
• Train LSTM-TrajGAN on GeoLife Beijing dataset
• Train LSTM-TrajGAN on Privamov Lyon dataset
• Train MARC on MDC dataset
• Train MARC on FourSquare NYC dataset
• Train MARC on GeoLife dataset
• Train MARC on Privamov dataset
• Compare MARC performance on real vs. generated MDC trajectories for LSTM-TrajGAN
• Get outputs from Yuting's LSTM-AE model on MDC, FourSquare, Privamov and GeoLife datasets
• Compare MARC performance on real vs. generated trajectories for LSTM-AE
• Evaluate realism of generated trajectories using distribution and distance comparisons