Spatial-Temporal Hypergraph Neural Network for Traffic Forecasting
Chengzhi Yao Zhi Li Junbo Wang
Traffic forecasting, which benefits from mobile Internet development and
position technologies, plays a critical role in Intelligent Transportation
Systems. It helps to implement rich and varied transportation applications and
bring convenient transportation services to people based on collected traffic
data. Most existing methods usually leverage graph-based deep learning networks
to model the complex road network for traffic forecasting shallowly. Despite
their effectiveness, these methods are generally limited in fully capturing
high-order spatial dependencies caused by road network topology and high-order
temporal dependencies caused by traffic dynamics. To tackle the above issues,
we focus on the essence of traffic system and propose STHODE: Spatio-Temporal
Hypergraph Neural Ordinary Differential Equation Network, which combines road
network topology and traffic dynamics to capture high-order spatio-temporal
dependencies in traffic data. Technically, STHODE consists of a spatial module
and a temporal module. On the one hand, we construct a spatial hypergraph and
leverage an adaptive MixHop hypergraph ODE network to capture high-order
spatial dependencies. On the other hand, we utilize a temporal hypergraph and
employ a hyperedge evolving ODE network to capture high-order temporal
dependencies. Finally, we aggregate the outputs of stacked STHODE layers to
mutually enhance the prediction performance. Extensive experiments conducted on
four real-world traffic datasets demonstrate the superior performance of our
proposed model compared to various baselines.