Optimizable adaptive multi-kernel support vector machine short-time traffic flow prediction method

Abstract

The invention relates to an optimizable adaptive multi-kernel support vector machine short-time traffic flow prediction method. A Gaussian kernel function and a polynomial kernel function are combined to construct an adaptive multi-kernel support vector machine (AMSVM); parameter optimization is performed on the AMSVM by using adaptive particle swarm optimization (APSO); historical data and real-time data are simultaneously considered, and a short-time traffic flow prediction model based on the AMSVM is put forward; a traffic flow data set is inputted, and a short-time traffic flow prediction result is generated by using the prediction model; and the prediction error is evaluated and analyzed according to the traffic flow prediction result and the actual traffic data. According to the method, the defect that the present support vector machine (SVM) method only uses a single kernel function for prediction can be improved so that the nonlinear and random change characteristics of the traffic flow can be fully adapted, real-time and adaptive prediction of the short-time traffic flow can be realized, the speed and the accuracy of the prediction result can be improved and thus the method has certain theoretical reference and practical significance.

Description

technical field [0001] The invention relates to the field of machine learning and intelligent transportation, in particular to an optimized short-term traffic flow prediction method of an adaptive multi-core support vector machine. Background technique [0002] Traffic flow guidance and control is the basic function of the Intelligent Transportation System (ITS). By releasing real-time and effective traffic travel information, it can induce travelers to choose the best travel route, avoid further agglomeration in congested areas, and guide and balance traffic flow in time and time. The distribution in space can realize the active control of traffic congestion, which can effectively improve the efficiency of road network traffic, alleviate urban traffic congestion, and reduce the resulting environmental pollution and resource waste. The core basis for the normal operation of the traffic flow induction and control system lies in the real-time, dynamic and accurate prediction o...

AI Technical Summary

Problems solved by technology

However, the existing SVM-based methods essentially only use a kernel function in the actual prediction, which is not enough to fully adapt to the changing characteristics of traffic flow

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