A deep spatio-temporal attention-based neural network for passenger flow prediction

Predicting the passenger flows in a city, especially in a metropolis, can guide traffic dispersion, and help assessing the risks of public safety and improving urban planning. However, it is challenging as passenger flows in a road network may vary with time and space, affected by weather conditions, urban activities, etc. In the paper, we propose a passenger flow prediction approach named Yildun, which constructs an encoder-decoder neural network and captures the spatial and temporal correlations inherent in passenger flows. More specifically, to predict the passenger flows at each and every station, a spatial attention mechanism is presented to adaptively extract inter-station correlations of flows by referring to the previous hidden state of the encoder at each time step. Meanwhile, a temporal attention mechanism is employed to capture time-dependent connections of flows by selecting relevant hidden states of the encoder across all time steps. Further, extra factors, such as POI (Point of Interest) data and day of the week, are fused in the decoder. With this spatio-temporal attention scheme, Yildun not only can make predictions effectively, but also is easily explainable. Extensive experiments are conducted on large-scale real-world data. The experimental results show that Yildun can predict passenger flows with small prediction errors and outperforms five baselines significantly.