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MF-STN: Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

This is the MXNet implementation of MF-STN in the following paper:

• Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, and Yu Zheng. Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction. 2019. In The 28th ACM International Conference on Information and Knowledge Management (CIKM’19), November 3–7, 2019, Beijing, China.

Requirements for Reproducibility

System Requirements:

• System: Ubuntu 16.04
• Language: Python 3.5.2
• Devices: a single GTX 1080 GPU

Library Requirements:

• scipy == 1.2.1
• numpy == 1.16.3
• pandas == 0.24.2
• mxnet-cu90 == 1.5.0
• dgl == 0.2
• tables == 3.5.1
• pymal
• h5py

Dependency can be installed using the following command:

pip install -r requirements.txt

Data Preparation

Unzip the data files in:

• TaxiBJ/data.zip
• TaxiNYC/data.zip

Description of Flow Data

The flow data are collected from Beijing and New York city. The shape of data are (date, hour, row, colume, flow_type).

Model Training & Testing

Given TaxiBJ task as example (TaxiNYC task is exactly the same as TaxiBJ task):

1. cd TaxiBJ/.
2. The settings of the models are in the folder src/model_setting, saved as yaml format. Four models and their enhanced versions by matrix factorization are provided: cnn, gru, conv_gru, and resnet.
3. All trained model will be saved in param/. There are two types of files in this folder:
   1. model.yaml: the model training log (the result on evaluation dataset of each epoch). This file records the number of the best epoch for the model.
   2. model-xxxx.params: the saved model parameters of the best evaluation epoch, where xxxx is the epoch number.
4. Running the codes:
   1. cd src/ .
   2. python train.py --file model_settting/[model_name].yaml --gpus [gpu_ids] --epochs [num_epoch]. The code will firstly load the best epoch from params/, and then train the models for [num_epoch]. Our code can be trained with multiple gpus. An example of [gpu_ids] is 0,1,2,3 if you have four gpus. But we recommend to use a single gpu to train & evaluate the model if possible.
5. Training from the begining:
   1. Remove the model records in param/, otherwise the code will train from the best pre-trained (saved) model.
   2. Train the model (example).
      1. Single gpu: python train.py --file model_setting/resnet_mf.yaml --gpus 0 --epochs 200.
      2. Multiple gpus: python train.py --file model_setting/resnet_mf.yaml --gpus 0,1 --epochs 200.
6. Testing the model (example): python train.py --file model_setting/resnet_mf.yaml --gpus 0 --epochs 0. The code will directly give evaluation results on evaluation dataset and test dataset, without training.

Citation

If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:

• Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, and Yu Zheng. Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction. 2019. In The 28th ACM International Conference on Information and Knowledge Management (CIKM’19), November 3–7, 2019, Beijing, China.

License

MF-STN is released under the MIT License (refer to the LICENSE file for details).