Seasonal adjustment in a SVR with chaotic simulated annealing algorithm traffic flow forecasting model

Inter-urban traffic flow forecasting has been one of most important issues in the research on road traffic congestion. However, the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. The support vector regression model (SVR) has been widely used to solve nonlinear time series problems. This investigation presents a traffic flow forecasting model by employing seasonal adjustment to deal with the cyclic (seasonal) traffic flow, in addition, the chaotic simulated annealing algorithm is also applied to optimize the three parameters of a SVR model, namely SSVRCSA, to forecast inter-urban traffic flow. A numerical example of traffic flow values from northern Taiwan is used to elucidate the forecasting performance. The results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), and seasonal Holt-Winters (SHW) models.