Long-term traffic flow prediction is a vital part of comprehensive transportation system planning, it is also an important basis for formulating macro traffic flow management policy. In order to improve the accuracy and efficiency of traffic sequence prediction, a hybrid prediction model based on singular spectrum analysis (SSA) is proposed, in which the problems of noise and unstable prediction of a single model in time series prediction are well solved. Firstly, raw data are reconstructed into tendency term, periodic term and residual term using SSA. Specifically, trend term is predicted using support vector regression ( SVR), and grey wolf optimization (GWO) algorithm is introduced to optimize parameters of the regression model. Moreover, periodic term is predicted using forgetting online sequential-extreme learning machine ( FOS-ELM). Finally, the predicted results are obtained by superimposing the above mentioned two parts. The experiment is carried out with real traffic flow data, and the mean absolute error (MAE) and root mean square error (RMSE) of the proposed hybrid prediction model are 215. 15 and 278. 51, respectively. The overall results show that the proposed model can solve the problems of large error fluctuation and unstable prediction of the single model. Furthermore, compared with empirical mode decomposition (EMD) and unprocessed time series, the prediction error of each model to the time series after singular spectrum analysis is reduced, which further illustrates the effectiveness of SSA in time series decomposition.