车流量预测
所属分类：Windows编程
开发工具：Python
文件大小：4779KB
下载次数：5
上传日期：2020-06-08 20:12:01
上 传 者：
星期八森减肥
说明： 在keras神经网络框架下，训练提前收集的车流量数据，对未来车流量预测。
(Under the framework of keras neural network, train the traffic data collected in advance to predict the future traffic.)
文件列表:
keras循环神经网络车流量预测\.ipynb_checkpoints\threeDimtest-checkpoint.ipynb (158403, 2019-10-17)
keras循环神经网络车流量预测\.ipynb_checkpoints\train-checkpoint.ipynb (20500, 2020-04-15)
keras循环神经网络车流量预测\.ipynb_checkpoints\twoDimtest-checkpoint.ipynb (92198, 2019-10-22)
keras循环神经网络车流量预测\.ipynb_checkpoints\岭回归-checkpoint.ipynb (37718, 2019-10-17)
keras循环神经网络车流量预测\data\.ipynb_checkpoints\data-checkpoint.ipynb (3206, 2019-05-28)
keras循环神经网络车流量预测\data\data.ipynb (4158, 2019-10-13)
keras循环神经网络车流量预测\data\data.py (1417, 2018-03-21)
keras循环神经网络车流量预测\data\test.csv (111037, 2019-05-07)
keras循环神经网络车流量预测\data\train.csv (199681, 2019-05-07)
keras循环神经网络车流量预测\data\__pycache__\data.cpython-36.pyc (1521, 2019-05-06)
keras循环神经网络车流量预测\data\__pycache__\data.cpython-37.pyc (1485, 2019-05-07)
keras循环神经网络车流量预测\images\BiLSTM.png (24608, 2019-05-29)
keras循环神经网络车流量预测\images\eva.png (70342, 2018-03-21)
keras循环神经网络车流量预测\images\GRU.png (19746, 2019-05-29)
keras循环神经网络车流量预测\images\LSTM.png (19500, 2019-05-29)
keras循环神经网络车流量预测\images\SAEs.png (38304, 2019-05-29)
keras循环神经网络车流量预测\LICENSE (1062, 2018-03-21)
keras循环神经网络车流量预测\model\Bilstm loss.csv (48807, 2019-10-12)
keras循环神经网络车流量预测\model\Bilstm.h5 (1095440, 2019-10-12)
keras循环神经网络车流量预测\model\bp loss.csv (48065, 2019-10-12)
keras循环神经网络车流量预测\model\bp.h5 (62768, 2019-10-12)
keras循环神经网络车流量预测\model\gru loss.csv (48798, 2019-10-12)
keras循环神经网络车流量预测\model\gru.h5 (323304, 2019-10-12)
keras循环神经网络车流量预测\model\lstm loss.csv (48602, 2019-10-12)
keras循环神经网络车流量预测\model\lstm.h5 (424192, 2019-10-12)
keras循环神经网络车流量预测\model\model.py (6400, 2020-04-15)
keras循环神经网络车流量预测\model\saes loss.csv (48772, 2019-10-12)
keras循环神经网络车流量预测\model\saes.h5 (2640576, 2019-10-12)
keras循环神经网络车流量预测\model\simplernn loss.csv (48548, 2019-10-12)
keras循环神经网络车流量预测\model\simplernn.h5 (123400, 2019-10-12)
keras循环神经网络车流量预测\model\__pycache__\model.cpython-36.pyc (4621, 2019-10-13)
keras循环神经网络车流量预测\model\__pycache__\model.cpython-37.pyc (3167, 2019-05-29)
keras循环神经网络车流量预测\threeDimtest.ipynb (155404, 2020-04-15)
keras循环神经网络车流量预测\train.ipynb (20500, 2020-04-15)
keras循环神经网络车流量预测\twoDimtest.ipynb (70925, 2020-04-15)
keras循环神经网络车流量预测\岭回归预测.ipynb (42539, 2019-10-17)
keras循环神经网络车流量预测\data\.ipynb_checkpoints (0, 2019-10-21)
keras循环神经网络车流量预测\data\__pycache__ (0, 2019-10-21)
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# 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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