%0 Journal Article %A LUO Xianglong %A GUO Huang %A LIAO Cong %A HAN Jing %A WANG Lixin %T Spatiotemporal Short-Term Traffic Flow Prediction Based on Broad Learning System %D 2022 %R 10.3778/j.issn.1002-8331.2011-0204 %J Computer Engineering and Applications %P 181-186 %V 58 %N 9 %X In order to solve the problem of long operation time in deep learning prediction model, the spatio-temporal correlation characteristic of traffic big data is mined sufficiently, a short-term traffic flow prediction model is proposed based on K-nearest neighbor(KNN) and broad learning system(BLS). KNN algorithm is used to choose K road sections, which has high spatio-temporal correlation with the predicted road section. Traffic flow data of selected road sections as the input of BLS model are used to predict respectively. The prediction results of selected sections are weighted, and the K prediction results with the minimum RMSE are taken as the final prediction values. The test results using dataset from the California PeMs show that the RMSE of the proposed model is reduced 46.56% compared with ARIMA, WNN, LSTM and KNN-LSTM model, the operation efficiency is improved obviously, thus KNN-BLS is an effective method for short-term traffic flow prediction. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2011-0204