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Project: TrafficFlowPrediction (GitHub Link)

• TrafficFlowPrediction-master
  • images
    • 原始图片
  • LICENSE
  • data
    • data.py
    • 100211_jiejiari_buhanling_test.csv
    • gongzuori.csv
    • data_volumn.py
    • 100211_buhanling_test.csv
    • zhoumo_train.csv
    • train.csv
    • 100211data
      • 100211_all_test.csv
      • 100211_weekend_train.csv
      • 100211_workdays_test(1).csv
      • 100211_weekend_test.csv
    • zhoumo.csv
    • gongzuori_test.csv
    • 100211_jiejiari_buhanling_train.csv
    • 100211_gongzuori_buhanling_test.csv
    • 5.7-5.9数据
    • zhoumo_test.csv
    • test.csv
  • train.py
  • README.md
  • model
    • 原始模型
      • lstm loss.csv
      • saes loss.csv
      • gru loss.csv
    • lstm loss.csv
    • 100211_workdays
      • lstm loss.csv
      • saes loss.csv
      • gru loss.csv
    • lstm_loss.csv
    • model.py
    • 100211_all
      • lstm loss.csv
      • saes loss.csv
      • gru loss.csv
    • 100211_weekend
      • lstm loss.csv
      • saes loss.csv
      • gru loss.csv
  • test.py
  • .gitignore

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.

Run command below to run the program:

python main.py
nihao