DeepMove
Predicting Human Mobility with Attentional Recurrent Networks.
https://github.com/vonfeng/DeepMove
Category: Consumption
Sub Category: Mobility and Transportation
Keywords
attention mobility-trajectory prediction www
Last synced: about 23 hours ago
JSON representation
Repository metadata
[WWW 2018] DeepMove: Predicting Human Mobility with Attentional Recurrent Network
- Host: GitHub
- URL: https://github.com/vonfeng/DeepMove
- Owner: vonfeng
- License: gpl-2.0
- Created: 2018-06-15T16:31:08.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-01-23T09:23:50.000Z (3 months ago)
- Last Synced: 2025-04-20T09:04:40.194Z (8 days ago)
- Topics: attention, mobility-trajectory, prediction, www
- Language: Python
- Homepage:
- Size: 143 MB
- Stars: 151
- Watchers: 2
- Forks: 56
- Open Issues: 6
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
💡Update
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.
DeepMove
PyTorch implementation of WWW'18 paper-DeepMove: Predicting Human Mobility with Attentional Recurrent Networks link
Datasets
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.
Requirements
- 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.
Project Structure
- /codes
- main.py
- model.py # define models
- sparse_traces.py # foursquare data preprocessing
- train.py # define tools for train the model
- /pretrain
- /data # preprocessed foursquare sample data (pickle file)
- /docs # paper and presentation file
- /resutls # the default save path when training the model
Usage
- 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.
| model_in_code | model_in_paper | top-1 accuracy (pre-trained) |
|---|---|---|
| markov | markov | 0.082 |
| simple | RNN-short | 0.096 |
| simple_long | RNN-long | 0.118 |
| attn_avg_long_user | Ours attn-1 | 0.133 |
| attn_local_long | Ours attn-2 | 0.145 |
- 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
Citation
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}
}
Owner metadata
- Name: Jie Feng
- Login: vonfeng
- Email:
- Kind: user
- Description: I am a researcher in urban science and spatio-temporal data mining.
- Website: https://vonfeng.github.io/
- Location: Beijing
- Twitter:
- Company: Tsinghua University
- Icon url: https://avatars.githubusercontent.com/u/11694233?v=4
- Repositories: 1
- Last ynced at: 2024-06-11T15:44:54.572Z
- Profile URL: https://github.com/vonfeng
GitHub Events
Total
- Issues event: 5
- Watch event: 15
- Issue comment event: 2
- Push event: 2
- Fork event: 2
Last Year
- Issues event: 5
- Watch event: 15
- Issue comment event: 2
- Push event: 2
- Fork event: 2
Committers metadata
Last synced: 7 days ago
Total Commits: 9
Total Committers: 1
Avg Commits per committer: 9.0
Development Distribution Score (DDS): 0.0
Commits in past year: 2
Committers in past year: 1
Avg Commits per committer in past year: 2.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| Jie Feng | f****e@h****m | 9 |
Committer domains:
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 12
Total pull requests: 1
Average time to close issues: over 1 year
Average time to close pull requests: N/A
Total issue authors: 12
Total pull request authors: 1
Average comments per issue: 1.17
Average comments per pull request: 0.0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
Past year pull requests: 0
Past year average time to close issues: about 1 month
Past year average time to close pull requests: N/A
Past year issue authors: 2
Past year pull request authors: 0
Past year average comments per issue: 1.0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
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- blldd (1)
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Top Pull Request Authors
- gunarto90 (1)
Top Issue Labels
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Score: 5.056245805348308