Traffic flow prediction method based on multivariable grey model time sequence

Abstract

The invention relates to a traffic flow prediction method based on a multivariable grey model time sequence. The method comprises the following steps: inputting collected observation station traffic flow, related external variable data and observation station information data; performing data preprocessing on the input data; inputting the data subjected to data preprocessing into a multivariable time sequence fusion prediction model based on data decomposition and a multivariable time sequence fusion prediction model based on result weighting for prediction; and comparing the predicted value with the actual value, and outputting a final result. The traffic flow of the expressway is predicted through fusion of multiple multivariable time sequence prediction models, the prediction precisionis improved, and through application to the expressway in the traffic field, the traffic management department can be helped to improve the intelligent management level, and the operation cost is reduced; through the display of the application demonstration system, data support can be visually provided for managers so as to make corresponding decisions in time and implement the decisions.

Description

technical field [0001] The present invention relates to a kind of traffic flow prediction method, particularly relate to a kind of traffic flow prediction method based on multivariate gray model time series. Background technique [0002] The main purpose of time series analysis and prediction is to establish a mathematical model for system operation records within a certain length range. This model can accurately analyze and fit the dynamic dependencies contained in various indicators of time series, and use it to predict the future value of the system. or behavior predictions. The prediction of time series can be studied from different angles and fields. There are classical time series analysis methods based on statistical methods, gray system theory for the study of uncertainty in the sequence, and computational intelligence technology-based Time Series Forecasting Techniques. [0003] Applying time series analysis and forecasting methods to traffic flow forecasting aims...

AI Technical Summary

Problems solved by technology

For the multivariate gray model, it has been applied in the fields of energy, economy, people's livelihood, etc., but it has not been shown in the field of traffic flow prediction. Since the univariate gray model can predict the traffic flow of expressway during holidays very well , then according to the characteristics of the data, how to improve the multivariate gray model and apply it to the field of traffic flow prediction to better improve the accuracy of traffic flow prediction is a problem that needs to be solved at present

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