access icon free SVRGSA: a hybrid learning based model for short-term traffic flow forecasting

Accurate and timely short-term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to complex non-linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non-linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper-parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.

Inspec keywords: learning (artificial intelligence); road traffic; search problems; time series; regression analysis; forecasting theory; support vector machines

Other keywords: short-term traffic flow forecasting; nonlinear data pattern; SVR model; poor forecasting accuracy; robust forecasting model; efficient forecasting model; time series prediction problems; hybrid learning based model; hybrid traffic flow forecasting model

Subjects: Other topics in statistics; Optimisation techniques; Knowledge engineering techniques; Optimisation techniques

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