JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Application of an Optimized Support Vector Regression Algorithm in Short-Term Traffic Flow Prediction |
| 영문제목(English Title) |
Application of an Optimized Support Vector Regression Algorithm in Short-Term Traffic Flow Prediction |
| 저자(Author) |
Ruibo Ai
Cheng Li
Na Li
|
| 원문수록처(Citation) |
VOL 18 NO. 06 PP. 0719 ~ 0728 (2022. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction 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 by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained. |
| 키워드(Keyword) |
Artificial Bee Colony Algorithm
Optimization
Prediction Algorithm
Short-time Traffic Flow
Support Vector Regression
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| 파일첨부 |
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