PyTorch implementation of the paper-"Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network", accepted by KDD 2022.

We are glad about your interest in our work and we would appreciate it if you cite our paper.

Datasets

• The processed data can be found in the "data" folder, which was processed by preproess.py and data_prepare.py.
• The raw data can be found at the following open source.
  • Foursqure (NYC and TKY)
  • Gowalla (Dallas)

Requirements

• Python>=3.8
• Pytorch>=1.8.1
• Numpy
• Pandas

Project Structure

• /data: file to store processed data
• /results: file to store results such as trained model and metrics.
• data_preprocess.py: data preprocessing to filter sparse users and locations (fewer than 10 records) and merge consecutive records (same user and location on the same day).
• data_prepare.py: data preparation for CSLSL (split trajectory and generate data).
• train_test.py: the entry to train and test a new model.
• evaluate.py: the entry to evalute a pretrained model.
• model.py: model defination.
• utils.py: tools such as batch generation and metric calculation.

Usage

1. Evaluate a pretrained model

python evaluate.py --data_name NYC --model_name model_NYC

2. Train and test a new model

python train_test.py --data_name NYC 

Detailed parameter description refers to evaluate.py and train_test.py