TrafficFlowPrediction-master
所属分类：其他书籍
开发工具：Python
文件大小：3278KB
下载次数：15
上传日期：2018-06-04 19:19:11
上 传 者：
HydraSheild
说明： 一种基于时间序列的交通流量预测模型，在python平台实现。
(A traffic volume forecasting model based on time series. Hope Help.)
文件列表:
LICENSE (1062, 2018-03-21)
data (0, 2018-06-04)
data\__pycache__ (0, 2018-06-04)
data\__pycache__\data.cpython-36.pyc (1509, 2018-06-04)
data\data.py (1417, 2018-03-21)
data\test.csv (111037, 2018-03-21)
data\train.csv (199681, 2018-03-21)
images (0, 2018-05-30)
images\GRU.png (22525, 2018-03-21)
images\LSTM.png (22817, 2018-03-21)
images\SAEs.png (40754, 2018-03-21)
images\eva.png (70342, 2018-03-21)
main.py (3371, 2018-03-21)
model (0, 2018-05-30)
model\gru loss.csv (48165, 2018-03-21)
model\gru.h5 (323336, 2018-03-21)
model\lstm loss.csv (48184, 2018-03-21)
model\lstm.h5 (424208, 2018-03-21)
model\model.py (2428, 2018-03-21)
model\saes loss.csv (48279, 2018-03-21)
model\saes.h5 (2640576, 2018-03-21)
train.py (3390, 2018-03-21)
# Traffic Flow Prediction
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
## Requirement
- Python 3.6
- Tensorflow-gpu 1.5.0
- Keras 2.1.3
- scikit-learn 0.19
## Train the model
**Run command below to train the model:**
```
python train.py --model model_name
```
You can choose "lstm", "gru" or "saes" as arguments. The ```.h5``` weight file was saved at model folder.
## Experiment
Data are obtained from the Caltrans Performance Measurement System (PeMS). Data are collected in real-time from individual detectors spanning the freeway system across all major metropolitan areas of the State of California.
device: Tesla K80
dataset: PeMS 5min-interval traffic flow data
optimizer: RMSprop(lr=0.001, rho=0.9, epsilon=1e-06)
batch_szie: 256
**Run command below to run the program:**
```
python main.py
```
These are the details for the traffic flow prediction experiment.
| Metrics | MAE | MSE | RMSE | MAPE | R2 | Explained variance score |
| ------- |:---:| :--:| :--: | :--: | :--: | :----------------------: |
| LSTM | 7.21 | ***.05 | 9.90 | 16.56% | 0.9396 | 0.9419 |
| GRU | 7.20 | 99.32 | 9.97| 16.78% | 0.9389 | 0.9389|
| SAEs | 7.06 | 92.08 | 9.60 | 17.80% | 0.9433 | 0.9442 |
## Reference
@article{SAEs,
title={Traffic Flow Prediction With Big Data: A Deep Learning Approach},
author={Y Lv, Y Duan, W Kang, Z Li, FY Wang},
journal={IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):865-873},
year={2015}
}
@article{RNN,
title={Using LSTM and GRU neural network methods for traffic flow prediction},
author={R Fu, Z Zhang, L Li},
journal={Chinese Association of Automation, 2017:324-328},
year={2017}
}
## Copyright
See [LICENSE](LICENSE) for details.
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