The invention relates to a 
hybrid depth learning model LSTM-ResNet based metropolitan space-time flow prediction technology. The invention can accurately predicting the change of urban spatio-temporaldata 
stream so as to provide important reference for 
urban management, and the key is to extract spatio-temporal dependency features from the data effectively. Currently, 
convolution neural network,which has been applied to spatio-temporal flow prediction, focuses on the extraction of 
spatial correlation features, ignoring the temporal dimension dependency and spatio-
temporal correlation features. In depth learning model, long and short memory network (LSTM) is suitable for dynamic modeling of 
time series, and residual 
convolution network (ResNet) is suitable for large-scale 
spatial correlation feature extraction. Therefore, we combine LSTM and ResNet to construct a 
hybrid depth-learning model for spatio-temporal flow prediction: LSTM is used to consider the 
time dependency before and after, and filter out the invalid time features; the output of LSTM is inputted into ResNet and the spatio-
temporal correlation feature is extracted. The model can automatically and accurately capture spatio-
temporal correlation features, especially retaining valid temporal features when considering forward and backward dependencies.