Application of an Optimized Support Vector Regression Algorithm in Short-Term Traffic Flow Prediction
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초록
The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the keytechnology in traffic flow induction systems. The research on short-term traffic flow prediction has showed theconsiderable social value. At present, the support vector regression (SVR) intelligent prediction model that issuitable for small samples has been applied in this domain. Aiming at parameter selection difficulty andprediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters,which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out bycomparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVRalgorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparingthe ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimizationSVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experimentsand verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing theABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracyof the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.
Abstract
The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the keytechnology in traffic flow induction systems. The research on short-term traffic flow prediction has showed theconsiderable social value. At present, the support vector regression (SVR) intelligent prediction model that issuitable for small samples has been applied in this domain. Aiming at parameter selection difficulty andprediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters,which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out bycomparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVRalgorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparingthe ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimizationSVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experimentsand verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing theABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracyof the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.
참고문헌 (15)
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