PSO-SVR: A Hybrid Short-term Traffic Flow Forecasting Method

Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. This paper presents a Hybrid PSO-SVR forecasting method to get a higher precision with less learning time; this method uses Particle Swarm Optimization (PSO) to search optimal SVR parameters. And to find a PSO that is more proper to SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flow out the searching space, according to comparison, one of the strategies can make PSO get the optimal parameters more quickly, this paper calls the PSO using this strategy as fast PSO. Furthermore, to handle the precision's decline caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The forecasting results of extensive comparison experiments indicate that proposed model can get more accurate forecasting result than other state-of-the-art algorithms; and when the data containing noises, the method with historical momentum still deserves accurate forecasting.