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Reliable traffic flow prediction is of great significance in traffic management and public safety. However, this is also a challenging task because it is affected by spatial dependencies, temporal dependencies and some additional factors (weather and emergencies, etc . ) . Most existing works can only consider part of the attributes of traffic data, resulting in insufficient modeling and unsatisfactory prediction performance. This paper proposed a novel end-to-end deep learning model, called spatio-temporal attention ConvLSTM ( ST-AttConvLSTM ), for traffic flow prediction. ST-AttConvLSTM was divided into three branches for modeling. For each branch, the residual neural network was used to extract local spatial features, and external factors were also combined with them. Then, it employed two components consisting of ConvlLSTM and attention model to discover the potential relationship of traffic flow, and capture the correlations of data in both spatial and temporal dimensions. It used two real trips data sets in Beijing and New York to evaluate the proposed method . The experimental results show that this method achieves higher prediction accuracy than well-known baselines. [ABSTRACT FROM AUTHOR]