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Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model

A traffic flow and combined model technology, which is applied in traffic flow detection, road vehicle traffic control system, traffic control system, etc., can solve the problem that wavelet neural network cannot effectively avoid overfitting

Pending Publication Date: 2021-05-07
LANZHOU UNIVERSITY OF TECHNOLOGY
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

Hou Q[37] proposed an adaptive short-term traffic flow forecast hybrid model, which uses linear autoregressive integrated moving average (ARIMA) method and nonlinear wavelet neural network (WNN) method to predict traffic flow, but the wavelet neural network The network cannot effectively avoid overfitting during training

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  • Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model
  • Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model
  • Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model

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Embodiment Construction

[0046] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] The short-term traffic flow forecasting system based on SARIMA-GA-Elman combined model includes the following steps:

[0048] 1) Modeling of SARIMA model;

[0049] The modeling of SARIMA model in described step 1) comprises the steps:

[0050] ① Judging whether the time series is stable or not based on the mean value, variance, and autocorrelation coefficient graph of the time series data;

[0051] ② Differentiate the non-stationary time series to reach a stationary state;

[0052] ③Determine the p, d, q, P, D, Q, S of the model by observing and drawing the autocorrelation coefficient map, partial autocorrelation coefficient map, heat map, etc.;

[0053] ④ Fit t...

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Abstract

The invention discloses a short-time traffic flow prediction system based on an SARIMA-GA-Elman combined model, and belongs to the technical field of traffic flow prediction of an intelligent traffic system. The method is realized through the five steps of modeling of an SARIMA model, Elman-RNN prediction, SARIMA-GA-Elman combined prediction, linear prediction and nonlinear prediction. The invention provides a novel optimization method to solve the problem that training of the neural network often consumes a large amount of time cost. The method comprises the following steps: firstly, training an Elman-RNN weight and a threshold value based on GA to obtain an interval near value of an optimal solution, and then continuously training by using an Elman-RNN gradient descent algorithm to obtain a final weight and a final threshold value; the optimization mode is very similar to transfer learning, and the training efficiency of the model can be improved. According to the method disclosed by the invention, the Elman-RNN nonlinear model is constructed by utilizing a prediction error subjected to SARIMA prediction. The prediction error excludes the interference of a linear factor and a daily cycle factor, and comprises the nonlinear characteristics of the traffic flow. The two models fully consider periodic, linear and nonlinear characteristics of traffic flow.

Description

technical field [0001] The invention belongs to the technical field of vehicle flow forecasting in intelligent traffic systems, and in particular relates to a short-term traffic flow forecasting system based on a SARIMA-GA-Elman combined model. Background technique [0002] The linear statistical model is a traffic flow prediction model established from the perspective of mathematical statistics to study the law of similarity between future data and historical data. Linear statistical models can only capture the linear characteristics of traffic flow, but cannot make predictions for complex nonlinear characteristics. Currently, they are usually combined with neural networks for prediction. [0003] Nonlinear theoretical models mainly include wavelet theory, fractal theory, catastrophe theory, and chaos theory. At present, the nonlinear theoretical model is not used as a single model to predict traffic flow, but more to be combined with other models to predict traffic flow. ...

Claims

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Application Information

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IPC IPC(8): G08G1/01G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06Q10/04G06Q50/26G08G1/0125
Inventor 张玺君王晨辉陶冶张冠男李积文郝俊余光杰钟云芳苏晋
Owner LANZHOU UNIVERSITY OF TECHNOLOGY
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