Short-term Traffic Flow Prediction Based on SOALSSVM

doi:10.3969/j.issn.1006-2475.2015.06.006

Abstract

Abstract: In view of the nonlinear and uncertainty for shortterm traffic flow characteristic, a forecasting model based on SOA-LSSVM is proposed, combining SOA’s advantages of high convergence precision with LSSVM’s superiority in fast solving speed. SOA is used in choosing regular parameter and nucleus parameter for LSSVM, thus, a optimal forecast model is established based on SOA-LSSVM. The model was used in shortterm traffic flow prediction, and the application results shows that compared with traditional ANN, SOA-LSSVM model has a better prediction precision and application effect, and this model is fit for shortterm traffic prediction with a good popularization value.

Key words: seeker optimization algorithm, least squares support vector machines, shortterm traffic flow, prediction

CLC Number:

TP306

ZHAO Wei. Short-term Traffic Flow Prediction Based on SOALSSVM[J]. Computer and Modernization, 2015, 0(6): 27-31.

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