Method for selecting state vector in nonparametric regression short-time traffic flow prediction

A short-term traffic flow and non-parametric regression technology, which is applied in traffic control systems, road vehicle traffic control systems, instruments, etc., can solve the problems of lack of research on prediction effects and unsatisfactory prediction effects, and achieve shortened operation Time, the effect of improving the prediction accuracy

Inactive Publication Date: 2011-03-02
TIANJIN UNIV
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[0003] However, the selection of state vectors that describe the causal relationship between the flow of the upstream road section and the road section to be tested mainly includes principal component analysis, correlation coefficient method, and autocorrelation coefficient. These methods are all analyzed from a statistical point of view. The factors related to the flow rate are used as the components of the state vector, but there is a lack of rese...

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  • Method for selecting state vector in nonparametric regression short-time traffic flow prediction
  • Method for selecting state vector in nonparametric regression short-time traffic flow prediction
  • Method for selecting state vector in nonparametric regression short-time traffic flow prediction

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[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] In order to solve the above problems, improve the prediction accuracy, shorten the running time, and meet the needs of practical applications, the embodiment of the present invention provides a method for selecting a state vector in short-term traffic flow prediction by non-parametric regression.

[0042] see figure 1, nonparametric regression is a data-driven heuristic forecasting mechanism that predicts future values ​​by searching historical databases for data similar to current observations. It can usually be divided into five components: selection of historical data, generation of sample database, definition of data similarity, K-nearest neighbor matching and prediction method. When using non-parametric regression to predict shor...

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Abstract

The invention discloses a method for selecting the state vector in the nonparametric regression short-time traffic flow prediction, relating to the technical field of short-time traffic flow prediction. At four conditions comprising peak hours, even hours, low hours and all the day, by using the method provided by the invention, the forecast accuracy, the stability, the speed and the transportability are improved, and the operation time is shortened, thus verifying the effectiveness and the necessity of the method provided by the invention.

Description

technical field [0001] The invention relates to the technical field of short-term traffic flow prediction, in particular to a method for selecting a state vector in non-parametric regression short-term traffic flow prediction. Background technique [0002] At present, many researchers at home and abroad have applied the non-parametric regression method to the study of short-term traffic flow prediction, and made necessary improvements to the non-parametric regression method according to the needs of practical problems. In 1991, Davis and Nihan really applied the method of non-parametric regression to traffic prediction. Although it avoided the problems of selecting models and parameter settings, the method required a large and representative historical database and the time consumed by the method running. longer. In 1995, Smith applied the non-parametric regression method to single-point short-term traffic flow prediction. The experimental results achieved better results th...

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IPC IPC(8): G08G1/00G08G1/052
Inventor 郑亮马寿峰贾宁朱宁王鹏飞
Owner TIANJIN UNIV
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