ROAM

This is the PyTorch implementation of Beyond Routines: Adaptive Mobility Prediction via Sequential-Relational Fusion.

Datasets

Note (for reproducibility).
In this repository release, we merge the original train and val splits and provide them as a single file train.npy to simplify deployment and reproduction. The test split remains unchanged.

For access to raw datasets, please refer to the Humob Challenge 2024. Follow the instructions on their website for data access and usage policies.

We processed the raw dataset by removing consecutive duplicate records in order to extract meaningful user activity locations. The processed data were originally split into train.csv, val.csv, and test.csv. In this repository, we provide the merged training split as train.npy for convenience.

All files contain the following five attributes, consistent with the original dataset:
uid, d, t, x, y.

Running Steps

• Optional arguments can be modified in train.py.
• To train the model, run:

  python train.py

• To evaluate the trained model, run:

  python train.py --test
  

Citation

@inproceedings{sun2026beyond,
  title={Beyond Routines: Adaptive Mobility Prediction via Sequential-Relational Fusion},
  author={Sun, Tianao and Liu, Ruizhe and Jia, Wenzhen and Zhao, Kai and Huang, Weiming and Chen, Meng},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1},
  year={2026}
}