Datasets
Standard Dataset
Traffic Flow Prediction and program codes
- Citation Author(s):
-
Hong-NingDai
Macau University of Science and Technology - Submitted by:
- Hong-Ning Dai
- Last updated:
- Mon, 06/15/2020 - 05:15
- DOI:
- 10.21227/ac0c-8043
- Data Format:
- Links:
- License:
1814 ViewsAbstract
This dataset mainly consists 1) source codes of wide-attention and deep model (WADC); 2) datasets to evaluate the performance of the proposed model. Datasets are obtained from the Caltrans Performance Measurement System (CPeMS) http://pems.doc.ca.gov; and Fremont Bridge Bicycle Counter (FBBC), https://data.seattle.gov.
For more details, please refer to our paper entitled "Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber-Physical Systems", accepted to appear in IEEE Transactions on Industrial Informatics and github https://github.com/zhoujunhao/wadc.
WADC
wide-attention and deep model
Installation
- Python 2.7
- Tensorflow-gpu 1.5.0
- Keras 2.1.3
- scikit-learn 0.19
Train the model
Run command below to train the model:
- Train the baseline single DL model based on CPeMS dataset.
python train_t.py --model model_name
You can choose "lstm", "gru" or "saes" as arguments. The .h5 weight file was saved at model folder.
- Train the composite DL model based on CPeMS dataset.
python train_wd.py --model model_name
You can choose "w_attention_d" (WADM) or "wd_crossLayer_attention" (DCN) as arguments. The .h5 weight file is saved at model folder.
- Training model based on FBBC dataset.
python train_bike.py --model model_name
You can choose "lstm", "gru" as arguments for training single DL model or choose "w_attention_d" (WADM) for training composite DL model.
Experiment
Data are obtained from the Caltrans Performance Measurement System (CPeMS) and Fremont Bridge Bicycle Counter (FBBC).
device: GTX 1050
dataset: CPeMS and FBBC
optimizer: RMSprop
Comments
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