Short-term traffic flow forecasting method based on three-layer K nearest neighbor

Abstract

The invention discloses a short-term traffic flow forecasting method based on three-layer K nearest neighbors. The short-term traffic flow forecasting method comprises the steps of: (1) counting traffic flow based on fixed time intervals and establishing a historical sample database; (2) evaluating shape similarity between a current point and points in the historical sample database by adopting similarity deviation degree and correlation coefficient respectively, and performing first-layer screening of points; (3) evaluating the points screened in the first layer according to hit rate and shape similar distance, and performing second-layer screening of points; (4) and evaluating matching distances between the current point and the points screened in the second layer by using an Euclidean distance method, and outputs a forecasting result by adopting a weighted mean value of inverse similar distance of a combination shape of the traffic flow at the corresponding next moment when nearest neighbor points are translated to the current point. The short-term traffic flow forecasting method adopts a two-layer shape similarity matching function, takes the shape matching distances between the nearest neighbor points and the current point into account, and improves accuracy and timeliness of short-term traffic flow forecasting.

Description

technical field [0001] The present invention relates to a short-term traffic flow prediction method, and more specifically, to a short-term traffic flow prediction method based on three-layer K-nearest neighbors. Background technique [0002] With the development of social economy and the continuous expansion of city scale, urban traffic problems are becoming more and more prominent. Intelligent transportation system is regarded as an important means to solve the problem of traffic congestion. With the advancement of relevant technologies in various fields of intelligent transportation, both traffic travelers and traffic managers are eager to obtain real-time and dynamic traffic operation status on the road. Real-time dynamic traffic allocation has become a key technology of intelligent transportation systems. Real-time dynamic traffic allocation needs to predict the traffic flow at the next decision-making time t+1 and several moments later at the time t when the control v...

AI Technical Summary

Problems solved by technology

However, the road traffic system is a time-varying and complex nonlinear system, and one of its notable features is its high degree of uncertainty, which makes the stability of the single K-nearest neighbor non-parametric regression prediction model for complex traffic flow prediction not high.

Some combined short-term traffic flow forecasting methods often have complex algorithms and a large amount of calculation, and cannot take into account the accuracy and real-time performance of the forecast at the same time.

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