In order to solve the problem that the traditional model and the machine learning model have poor performance in predicting the periodic time series, this study proposes the SSA-LightGBM prediction model which takes the Hancheng expressway traffic volume survey data as the data set. First, the Passenger Car Unit (PCU) of Hancheng expressway data is calculated. Second, the singular spectral analysis is applied on the PCU data to obtain periodic and random terms, the periodic term is reconstructed, and then the LightGBM is used to predict the random term. Finally, the predicted random term and the periodic extension signal are superimposed to obtain the prediction data of final expressway PCU. At the same time, compared with the prediction results of XGBoost and LightGBM model, the prediction results of SSA-LightGBM are closest to the true values, and MAE, RMSE and R² are better. This result has a good guiding significance for the research of the forecast on future change trend of our country’s expressway volume survey data, and provides a good reference value for the renovation and maintenance of our country’s expressways.