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

Copyright

See LICENSE for details.