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

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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Problems solved by technology

[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 research on whether the state vector is selected and whether the prediction effect is improved

It is worth noting that even if the running time of the method is shortened by improving the storage mode of the historical database and the neighbor search method, if the selection of the state vector is not enough to describe the flow causality between the upstream road section and the road section to be tested, then the final prediction effect not enough to satisfy

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