We are excited to announce AgentMove, an LLM-based agentic framework designed for zero-shot mobility prediction. Leveraging the world knowledge and sequential modeling capabilities of LLMs, AgentMove paves the way for a promising new direction in mobility prediction.

PyTorch implementation of WWW'18 paper-DeepMove: Predicting Human Mobility with Attentional Recurrent Networks link

The sample data to evaluate our model can be found in the data folder, which contains 800+ users and ready for directly used. The raw mobility data similar to ours used in the paper can be found in this public link.

• Python 2.7
• Pytorch 0.20

cPickle is used in the project to store the preprocessed data and parameters. While appearing some warnings, pytorch 0.3.0 can also be used.

• /codes
  • main.py
  • model.py # define models
  • sparse_traces.py # foursquare data preprocessing
  • train.py # define tools for train the model
• /pretrain
  • /simple
    • res.m # pretrained model file
    • res.json # detailed evaluation results
    • res.txt # evaluation results
  • /simple_long
  • /attn_local_long
  • /attn_avg_long_user
• /data # preprocessed foursquare sample data (pickle file)
• /docs # paper and presentation file
• /resutls # the default save path when training the model

1. Load a pretrained model:

python main.py --model_mode=attn_avg_long_user --pretrain=1

The codes contain four network model (simple, simple_long, attn_avg_long_user, attn_local_long) and a baseline model (Markov). The parameter settings for these model can refer to their res.txt file.

2. Train a new model:

python main.py --model_mode=attn_avg_long_user --pretrain=0

Other parameters (refer to main.py):

• for training:
  • learning_rate, lr_step, lr_decay, L2, clip, epoch_max, dropout_p
• model definition:
  • loc_emb_size, uid_emb_size, tim_emb_size, hidden_size, rnn_type, attn_type
  • history_mode: avg, avg, whole

If you find this work helpful, please cite our paper.

@inproceedings{feng2018deepmove,
  title={Deepmove: Predicting human mobility with attentional recurrent networks},
  author={Feng, Jie and Li, Yong and Zhang, Chao and Sun, Funing and Meng, Fanchao and Guo, Ang and Jin, Depeng},
  booktitle={Proceedings of the 2018 world wide web conference},
  pages={1459--1468},
  year={2018}
}