MobilityGen: DDPM for human mobility behavior

This repository represents the implementation of the paper:

Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal
| MIE, ETH Zurich | FRS, Singapore-​ETH Centre | PRS, ETH Zurich |

Requirements and dependencies

This code has been tested on

• Python 3.11, Geopandas 0.14.4, trackintel 1.3.1, PyTorch 2.3.1, cudatoolkit 11.8, transformers 4.41.2, GeForce RTX 3090 and GeForce RTX 4090

To create a virtual environment and install the required dependences please run:

    git clone https://github.com/mie-lab/mobility_generation
    cd mobility_generation
    conda env create -f environment.yml
    conda activate mobility_generation
    pip install -e .
    pip install -e improved-diffusion

in your working terminal. The MobilityGen implementation is built on top of the improved-diffusion package by https://github.com/openai/improved-diffusion.

For projecting locations into s2grid, the s2geometry Python package needs to be build from source and available in the virtual environment. Check the installation guide in https://github.com/google/s2geometry.

Folder structure

The respective code files are stored in seperate modules:

• /preprocess/*. Functions that are used for preprocessing the dataset. Should be executed before training a model. 01_project_s2.py includes functions to project locations into s2geometery locations (require the s2geometry library to be installed, see Requirements and dependencies). 02_poi.py includes POI preprocessing and embedding methods (LDA). 10_geolife.py and 11_generate_data_geolife.py include functions for preprocessing the raw Geolife dataset into formats that are acceptable by MobilityGen (See Reproducing on the Geolife dataset)
• /diffusion/*. Implementation of MobilityGen model.
• /config/*. Hyperparameter settings are saved in the .yml files under the respective dataset folder under config/. For example, /config/diff_geolife.yml contains hyperparameter settings for the geolife dataset.
• /utils/*. Helper functions that are used for model training.
• /evaluate/*. Evaluation of the simulated sequences using MobilityGen. entropy.py includes functions to calculate the random, uncorrelated and real entropy. stats.py includes functions to calculate the mobility motifs.

Reproducing on the Geolife dataset

The results in the paper are obtained from the MOBIS dataset that are not publicly available. We provide a runnable example of the pipeline on the Geolife dataset. The travel mode of the Geolife users are determined through the provided mode labels and using the trackintel function trackintel.analysis.predict_transport_mode. The steps to run the pipeline are as follows:

1. Install dependencies

• Download the repo, install neccessary Requirements and dependencies.

2. Download Geolife

• Download the Geolife GPS tracking dataset from here. Unzip and copy the Data folder into data/geolife/. The file structure should look like data/geolife/Data/000/....
• Create file paths.json, and define your working directories by writing:

{
    "raw_geolife": "./data/geolife"
}

3. Preprocess the dataset

• run

    python preprocess\10_geolife.py --epsilon 20

for obtaining the staypoints and location sequences for the geolife dataset. Note that the locations are directly generated from user tracking data, without the s2geometry projection. The process takes 15-30min. loc_geolife.csv and sp_geolife_all.csv will be created under data/ folder.

• run

    python preprocess/11_generate_data_geolife.py --src_min_days 7 --src_max_days 21 --tgt_min_days 3 

for obtaining the train, validation, and test staypoint sequences (source sequence and target sequences). Due to the data quality of OSM at the tracking period, we choose not to attached POIs to the locations. train_7_3_geolife.pk, valid_7_3_geolife.csv, and test_7_3_geolife.pk will be created under data/ folder.

4. Run the MobilityGen model

• Configure the network parameters in config/diff_geolife.yml, and run

    python run_diff.py --config config/diff_geolife.yml

for starting the training process.

• Alternatively, specify the number of GPUs with nproc_per_node for training with Distributed Data Parallel:

    python -m torch.distributed.launch --nproc_per_node=1 run_diff.py --config config/diff_geolife.yml

The configuration of the current run, the network paramters and the log information will be stored under run/ folder.

5. Simulate sequences with trained MobilityGen model

With a trained model, new sequences can be obtained with the parameters defined in config/diff_sample_geolife.yml. In the config file, specify the path and name of the trained model, and run

    python run_diff_sample.py --config config/diff_sample_geolife.yml

for starting the simulation process. Simulated traces will be stored json format under run/ folder.

Contact

If you have any questions, please let me know:

• Ye Hong {[email protected]}