STG2Seq: Spatial-Temporal Graph to Sequence Model
for Multi-step Passenger Demand Forecasting
Abstract
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
However, predicting passenger demand over multiple time horizons is generally challenging due to
the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multistep citywide passenger demand prediction based
on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a shortterm encoder to derive the next-step prediction for
generating multi-step prediction; 3) an attentionbased output module to model the dynamic temporal and channel-wise information. Experiments on
three real-world datasets show that our model consistently outperforms many baseline methods and
state-of-the-art models