Short term prediction of sector traffic is the premise of accurately implementing sector traffic optimization and management measures. Based on the decomposition integration prediction methodology， a vibrational mode decomposition least square support vector machine（VMD-LSSVM） prediction model is established. Firstly， the VMD method is applied to decompose the traffic into several sectors. Then， the LSSVM model is used to predict the modes . The modal prediction results are added and integrated to obtain the final prediction value. The calculation results show that the prediction accuracy of the VMD-LSSVM model is 0.97 in 1—6 h and 0.94 in 7—12 h. Compared with the first mock exam model of autoregressive integrated moving average model（ARIMA）， back propagation（BP） and LSSVM， the prediction accuracy of the VMD-LSSVM model 1—6 h increased by 11.5%， 7.8%， 4.3%， respectively， and 2.1%， 6.6%， 5.4% compared with compete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）-LSSVM， CEEMDAN-BP and VMD-BP， respectively. Compared with 30 min and 15 min statistical scales， the prediction accuracy is improved by 6.6% and 19.8%， respectively. For the eight experiments of time universality， the prediction accuracy is more than 0.94. For the experiments of 27 sectors， the prediction accuracy of 24 sectors is more than 0.9. The example results show that the VMD-LSSVM model has good prediction performance and good universality， and it is feasible and effective for short-term prediction of sector traffic.