关键词: 短时交通流, 预测模型, 相空间重构, 支持向量回归机

Abstract: Predicting short time traffic flow needs phase reconstruction at first, and then the traffic flow is computed with prediction model of time series. Support Vector Regression（SVR） is a popular forecasting model that has better generality with more powerful theoretical base. However, the embedding dimensions and time delay of phase reconstruction and the parameters of SVR are computed independently, which is difficult to get the optimal parameters in the meanwhile so that accuracy of prediction is not good. In order to improve the accuracy of prediction, a method of short time prediction is proposed which uses combination of phase reconstruction and Support Vector Regression. In this model, the parameters of phase reconstruction and Support Vector Regression are optimized with combination using the PSO. The experiment conducted by traffic dataset has shown that the new method improves the performance of short-term traffic flow forecasting model.

Key words: short-term traffic flow, prediction model, phase space reconstruction, Supporting Vector Regression（SVR）