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Multi-step passenger demand forecasting is a crucial task in on-demandvehicle sharing services. However, predicting passenger demand over multipletime horizons is generally challenging due to the nonlinear and dynamicspatial-temporal dependencies. In this work, we propose to model multi-stepcitywide passenger demand prediction based on a graph and use a hierarchicalgraph convolutional structure to capture both spatial and temporal correlationssimultaneously. Our model consists of three parts: 1) a long-term encoder toencode historical passenger demands; 2) a short-term encoder to derive thenext-step prediction for generating multi-step prediction; 3) anattention-based output module to model the dynamic temporal and channel-wiseinformation. Experiments on three real-world datasets show that our modelconsistently outperforms many baseline methods and state-of-the-art models.

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting