关键词: 出租车流量预测, 时空特征, 卷积神经网络, 门控循环单元, 注意力机制

Abstract: Traditional passenger flow forecasting research only focuses on the time-series characteristics of data and ignores the spatial dimension characteristics， weather or other external factors. This paper proposes a convolution gated spatio-temporal forecasting model (KSTCGN) combined with attention mechanism to predict taxi passenger flow. In this model， convolutional neural network (CNN) is used to extract the spatial features of the traffic in each period of the grid， and gated recurrent unit (GRU) is used to extract the temporal features of the passenger traffic. The convolution layer introduces CBAM attention mechanism to pay more attention to important spatial points. GRU layer combines attention mechanism to focus on the time period that has an important impact on traffic， and uses K-means clustering algorithm to cluster different time periods. Through experimental analysis and comparison with other traditional prediction algorithms， it is proved that the proposed combined model can effectively improve the prediction accuracy.

Key words: taxi traffic forecast； spatio-temporal characteristics； convolutional neural network； gated recurrent , unit； attention mechanism