https://github.com/wondervictor/traffic_prediction
Traffic Prediction in PaddlePaddle (ASC17 Deep Learning Application)
https://github.com/wondervictor/traffic_prediction
deep-learning deep-neural-networks machine-learning predictive-analysis
Last synced: 28 days ago
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Traffic Prediction in PaddlePaddle (ASC17 Deep Learning Application)
- Host: GitHub
- URL: https://github.com/wondervictor/traffic_prediction
- Owner: wondervictor
- License: apache-2.0
- Created: 2017-02-20T15:56:52.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-04-27T16:36:05.000Z (about 8 years ago)
- Last Synced: 2025-04-15T22:58:49.038Z (28 days ago)
- Topics: deep-learning, deep-neural-networks, machine-learning, predictive-analysis
- Language: Python
- Homepage:
- Size: 6.88 MB
- Stars: 17
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Traffic Prediction
**A Deep Learning Prediction Task Supported by PaddlePaddle**
### Introduction
1. Construct a easy Graph model by combine the adjacent nodes together
* Collect spatio information from `graph.csv`, then construct a Center- MultiOrder-Adjacent Nodes Model (中心-多阶邻接节点图模型)
* MultiOrder adjacent nodes can lengthen the predicting period
2. ResLSTM (Residual Long Short Term Memory)
* Inpired by ResNet(which can deepen the neural network to more than 150 layers) ([Deep Residual Network](https://arxiv.org/abs/1512.03385))
* LSTM can deal with time sequence well
* Residual can contribute to the loss and reasonable gradient
3. Construct the full network
* Input speed sequences
* Bidirectional LSTM can learn from the former speed and latter speed to deal with the relation of the adjacent nodes and the center node.
* Use fully connected layers to extract the spatio feature.
* End-to-End Trainable
* LSTM and ReLU can lower the possibility of Gradient Vanishing
### Usage
#### Install PaddlePaddle
[go to the PaddlePaddle documentation](http://www.paddlepaddle.org/doc/build/build_from_source.html#build-and-install)
##### Prepare data
```
cd data
```
* get point list for training (Graph Constructing)
```
points_to_point()
get_points_count_list_2()
```
* remove zeros
```
split_data.split_by_remove_some_timestamps('speeds.csv',[(from_time, to_time)],'speed_nzero.csv')
```
* validation set
```
split_data.split_out(filename, [(from_time, to_time)],[output filenames])
```
* test data
```
split_data.get_test_data('test_speeds.csv', 'train_speeds.csv', 'speed_no_valid.csv', [(from_time, to_time)]) dataset = create_dataset('test_speeds.csv')
```
* create data set
```
dataset = create_dataset('test_speeds.csv')
get_speed_data_2(dataset, 'test')
dataset = create_dataset('train_speeds.csv')
get_speed_data_2(dataset, 'train')
dataset = create_dataset('speeds_without_zero.csv')
get_predict_data_2(dataset)
dataset = create_dataset('VadiationSet/419_6_10.csv')
get_predict_valid(dataset)
```
##### run
```
sh run.sh
```
### Advantages
* End-to-End trainable
* Maybe high accuracy :)
* Generalization ability and robustness
* make full use of data provided
### Drawbacks
* training speed
* Framework (PaddlePaddle) is too slow and stupid
### Licence
**This project is under the Apache-2.0**