https://github.com/jonnoftw/traffic-prediction
Predict traffic flow with LSTM. For experimental purposes only, unsupported!
https://github.com/jonnoftw/traffic-prediction
experimental hyperas hyperopt lstm traffic-prediction
Last synced: about 1 year ago
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Predict traffic flow with LSTM. For experimental purposes only, unsupported!
- Host: GitHub
- URL: https://github.com/jonnoftw/traffic-prediction
- Owner: JonnoFTW
- Created: 2016-09-22T07:39:22.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2018-07-19T02:56:36.000Z (almost 8 years ago)
- Last Synced: 2025-02-25T07:41:35.224Z (over 1 year ago)
- Topics: experimental, hyperas, hyperopt, lstm, traffic-prediction
- Language: Python
- Homepage:
- Size: 30.3 KB
- Stars: 55
- Watchers: 4
- Forks: 22
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# LSTM Based Traffic Prediction
This project seeks to use LSTM for traffic prediction using the [Keras](https://keras.io/) frontend for [Theano](http://deeplearning.net/software/theano/). Hyperparameter optimisation is used to find the best set of parameters for the network.
### Usage
Run:
```
pip install -r requirements.txt
```
Then edit [main.py](https://github.com/JonnoFTW/traffic-prediction/blob/master/main.py) so that it uses your own parameters for the network. It will attempt to store hyperparameter results in mongodb. You can use [show_results.py](https://github.com/JonnoFTW/traffic-prediction/blob/master/show_results.py) to view them. Please keep in mind this for experimentation only and not appropriate for production.
Run using:
```
python main.pymain.py
```
The CSV should have a the following format:
```
timestamp,16,17,18,19,20,21
2011-12-31 23:55:00,4,6,8,13,3,0
2012-01-01 00:00:00,5,7,8,10,3,2
2012-01-01 00:05:00,3,3,7,15,1,2
2012-01-01 00:10:00,9,3,3,7,1,1
2012-01-01 00:15:00,3,4,12,15,2,0
2012-01-01 00:20:00,7,5,11,19,2,2
```
Basically, the first column must be a timestamp and the rest integer values.
Scripts are also provided to speed up processing via Sun Grid Engine. Though they are unreliable because of the length of time it takes for theano to compile the models. You will also need CUDA and a compatible card if you want the speedups they provide.