All Projects → xiaochus → Trafficflowprediction

xiaochus / Trafficflowprediction

Licence: mit
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

Programming Languages

python
139335 projects - #7 most used programming language

Labels

Projects that are alternatives of or similar to Trafficflowprediction

Tensorflow Sentiment Analysis On Amazon Reviews Data
Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.
Stars: ✭ 34 (-85.95%)
Mutual labels:  lstm, gru
See Rnn
RNN and general weights, gradients, & activations visualization in Keras & TensorFlow
Stars: ✭ 102 (-57.85%)
Mutual labels:  lstm, gru
Rnn Notebooks
RNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)
Stars: ✭ 48 (-80.17%)
Mutual labels:  lstm, gru
Easy Deep Learning With Keras
Keras tutorial for beginners (using TF backend)
Stars: ✭ 367 (+51.65%)
Mutual labels:  lstm, gru
Haste
Haste: a fast, simple, and open RNN library
Stars: ✭ 214 (-11.57%)
Mutual labels:  lstm, gru
Cryptocurrencyprediction
Predict Cryptocurrency Price with Deep Learning
Stars: ✭ 453 (+87.19%)
Mutual labels:  lstm, gru
Gdax Orderbook Ml
Application of machine learning to the Coinbase (GDAX) orderbook
Stars: ✭ 60 (-75.21%)
Mutual labels:  lstm, gru
lmkit
language models toolkits with hierarchical softmax setting
Stars: ✭ 16 (-93.39%)
Mutual labels:  lstm, gru
Load forecasting
Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models
Stars: ✭ 160 (-33.88%)
Mutual labels:  lstm, gru
Rnn Text Classification Tf
Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification
Stars: ✭ 114 (-52.89%)
Mutual labels:  lstm, gru
Theano lstm
🔬 Nano size Theano LSTM module
Stars: ✭ 310 (+28.1%)
Mutual labels:  lstm, gru
Eeg Dl
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Stars: ✭ 165 (-31.82%)
Mutual labels:  lstm, gru
Pytorch-POS-Tagger
Part-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN
Stars: ✭ 24 (-90.08%)
Mutual labels:  lstm, gru
Sudl
light deep neural network tools box(LSTM,GRU,RNN,CNN,Bi-LSTM,etc)
Stars: ✭ 29 (-88.02%)
Mutual labels:  lstm, gru
Deep-Learning-for-Expression-Recognition-in-Image-Sequences
The project uses state of the art deep learning on collected data for automatic analysis of emotions.
Stars: ✭ 26 (-89.26%)
Mutual labels:  lstm, gru
Tensorflow Lstm Sin
TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction
Stars: ✭ 52 (-78.51%)
Mutual labels:  lstm, gru
Manhattan-LSTM
Keras and PyTorch implementations of the MaLSTM model for computing Semantic Similarity.
Stars: ✭ 28 (-88.43%)
Mutual labels:  lstm, gru
dts
A Keras library for multi-step time-series forecasting.
Stars: ✭ 130 (-46.28%)
Mutual labels:  lstm, gru
Pytorch Rnn Text Classification
Word Embedding + LSTM + FC
Stars: ✭ 112 (-53.72%)
Mutual labels:  lstm, gru
Pytorch Kaldi
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
Stars: ✭ 2,097 (+766.53%)
Mutual labels:  lstm, gru

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 98.05 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

evaluate

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 for details.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].