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Traffic flow prediction method based on feature reconstruction error

A traffic flow and reconstruction error technology, applied in the field of machine learning, can solve the problems of low feasibility of industrial application of traffic flow prediction, large calculation and space complexity, etc., and achieve the effect of stable model prediction and small space complexity

Active Publication Date: 2020-10-09
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

These works by changing the weight of training samples need to maintain a weight matrix with the same size as the number of samples, which makes its calculation and space complexity in large-scale industrial applications very large. Therefore, these works are important in industrial applications of traffic flow forecasting. less feasible

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  • Traffic flow prediction method based on feature reconstruction error
  • Traffic flow prediction method based on feature reconstruction error
  • Traffic flow prediction method based on feature reconstruction error

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Embodiment Construction

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention, but do not limit it in any way.

[0031] Such as figure 1 As shown, a traffic flow prediction method based on feature reconstruction error, including:

[0032] S01. Initialize target machine learning network parameters.

[0033] The target machine learning network can be most commonly used deep neural network models such as space-time graph network (ST-GCN), or it can be a linear machine learning network such as least squares network (OLE).

[0034] In this embodiment, the least squares network (OLE) is used as the basic network to predict traffic flow as an example. At the same time, the initialization of the model parameters is obtained by Gaussian distribution sampling.

[0035] S02, initialize the feature correction weigh...

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Abstract

The invention discloses a traffic flow prediction method based on a feature reconstruction error, and belongs to the technical field of machine learning. The method comprises the steps of: (1) selecting a target machine learning network, and initializing the parameters of the target machine learning network; (2) constructing a training data set of traffic flow, and initializing parameters of a feature correction weight matrix; (3) training the feature correction weight matrix by using the training data set, and using a random gradient descent algorithm and a feature reconstruction error loss function in the training process; (4) fixing feature correction weight matrix parameters, training a target machine learning network, and using a random gradient descent algorithm in the training process; (5) repeating the step (3) and the step (4) until the loss function converges or reaches a maximum training step number; and (6) after the training is finished, inputting the traffic flow data tobe predicted into the trained network model to obtain the predicted traffic flow. According to the invention, the stability of the model in traffic flow prediction can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a traffic flow prediction method based on feature reconstruction errors. Background technique [0002] The training process of the traffic flow prediction model based on neural network requires that the data samples follow the independent and identical distribution assumption (i.i.d assumption), that is, the training and testing data sets are sampled from the same data distribution. Under the assumption of i.i.d., the trained model can be directly applied to the test data set, and can obtain the same effect as the training data set. Although this approach has proven to be very successful in many research public datasets, it is flawed in practical applications. The reason is the data selection bias that is ubiquitous in practical applications, that is, it is impossible to guarantee that the training or testing samples are completely randomly sampled. Therefo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06F30/27G06N3/08G06Q10/04
CPCG06N3/08G06Q10/04G08G1/01G06F30/27
Inventor 余正旭蔡登王鹏飞徐骏凯金仲明黄建强华先胜何晓飞
Owner ZHEJIANG UNIV
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