A spatio-temporal sequence-to-sequence network for traffic flow prediction

Abstract

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. STSSN is based on an encoder-decoder framework. In the network, the model’s input is processed to extract the periodic daily and weekly patterns in traffic flows. Both the encoder and decoder mainly consist of an enhanced diffusion convolutional network (EDCN) and a temporal convolutional network (TCN). In the EDCN module, the diffusion convolution incorporates time-varying node representations so as to capture both node-specific patterns and time-varying spatial correlations. In the TCN module, we take full advantage of the parallel computing in the dilated causal convolution to mine local (short-term) temporal correlations. More importantly, global (long-term) temporal correlations are discovered through an encoder-decoder attention (EDA) module. This EDA mechanism directly models the relationship between the encoder and decoder to mitigate problems with error propagation. Experiments on two real-world datasets verify the superiority of STSSN, with STSSN’s MAE at between 3.85%-6.17% lower than the state-of-the-art baselines on the PEMS-BAY dataset.