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.
SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting
IET Intelligent Transport Systems ; 13 , 9 ; 1348-1355
2019-09-01
8 pages
Article (Journal)
Electronic Resource
English
time series prediction problems , nonlinear data pattern , regression analysis , efficient forecasting model , time series , poor forecasting accuracy , forecasting theory , hybrid traffic flow forecasting model , road traffic , search problems , hybrid learning based model , SVR model , learning (artificial intelligence) , support vector machines , short‐term traffic flow forecasting , robust forecasting model
SVRGSA: a hybrid learning based model for short-term traffic flow forecasting
IET | 2019
|Hybrid dual Kalman filtering model for short‐term traffic flow forecasting
Wiley | 2019
|