%0 Journal Article %A ZHANG Zhuangzhuang %A QU Licheng %A LI Xiang %A ZHANG Minghao %A LI Zhaolu %T Traffic Flow Prediction with Missing Data Based on Spatial-Temporal Convolutional Neural Networks %D 2022 %R 10.3778/j.issn.1002-8331.2108-0329 %J Computer Engineering and Applications %P 259-265 %V 58 %N 7 %X In order to improve the accuracy of traffic flow prediction under the condition of continuous missing data, a spatial-temporal convolutional neural network prediction algorithm is proposed. The spatial-temporal matrix of road network traffic data is established by considering the spatial adjacent relationship of horizontal distribution and the time dependence of vertical distribution. A mask matrix is introduced to represent the missing situation of data. The convolution operation is used to mine the implicit nonlinear correlation between the upstream and downstream detectors, establish the mapping relationship between the current and future traffic states, and realize the traffic flow prediction under the condition of continuous data absence. The experimental results show that the accuracy of traffic flow prediction is better than those of LSTM, GRU and GMN models in the absence of continuous traffic data. The model shows good stability and robustness, and improves the accuracy of traffic flow prediction in the absence of data. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2108-0329