Application of SVR with Genetic optimization algorithm in urban traffic flow forecasting

Forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Due to traffic flow forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. This investigation presents a short-term traffic forecasting model which combines the support vector regression (SVR) model with Genetic Optimization algorithms (SVRGA) to forecast inter-urban traffic flow. Additionally, a numerical example is employed to elucidate the forecasting performance of the proposed SVRGA model. Finally the results compare and their performance with time series models.

Application of SVR with Genetic optimization algorithm in urban traffic flow forecasting Keywords:

Traffic flow forecasting , Support vector regression (SVR) , Genetic Optimization algorithms (GA)