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Spatio-temporal prediction has drawn much attention given its wide application, of which traffic flow prediction is a typical task. Within the vision of smart cities, traffic flow prediction plays a vital role in traffic control and optimization. The current approaches commonly use a graph convolutional network (GCN) to capture any spatial correlations and a recurrent neural network (RNN) to mine any temporal correlations. However, GCNs cannot detect spatial heterogeneity and time-varying spatial correlations, and RNNs cannot model the periodicity of traffic series data. Further, iterative training of RNNs may come at a high computational cost and result in problems with error propagation. To this end, we propose STSSN, a spatio-temporal sequence-to-sequence network, that not only explores heterogeneous and time-varying spatial correlations, but also efficiently exploits sequential and periodic temporal correlations …