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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

  1. ResLSTM (Residual Long Short Term Memory)

    • Inpired by ResNet(which can deepen the neural network to more than 150 layers) (Deep Residual Network)
    • LSTM can deal with time sequence well
    • Residual can contribute to the loss and reasonable gradient

  1. 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

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

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Traffic Prediction in PaddlePaddle (ASC17 Deep Learning Application)

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