Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting
Yirong Chen Ziyue Li Wanli Ouyang Michael Lepech
Accurate traffic forecasting is vital to intelligent transportation systems,
which are widely adopted to solve urban traffic issues. Existing traffic
forecasting studies focus on modeling spatial-temporal dynamics in traffic
data, among which the graph convolution network (GCN) is at the center for
exploiting the spatial dependency embedded in the road network graphs. However,
these GCN-based methods operate intrinsically on the node level (e.g., road and
intersection) only whereas overlooking the spatial hierarchy of the whole city.
Nodes such as intersections and road segments can form clusters (e.g.,
regions), which could also have interactions with each other and share
similarities at a higher level. In this work, we propose an Adaptive
Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting by
exploiting the spatial hierarchy and modeling multi-scale spatial correlations.
Apart from the node-level spatiotemporal blocks, AHSTN introduces the adaptive
spatiotemporal downsampling module to infer the spatial hierarchy for
spatiotemporal modeling at the cluster level. Then, an adaptive spatiotemporal
upsampling module is proposed to upsample the cluster-level representations to
the node-level and obtain the multi-scale representations for generating
predictions. Experiments on two real-world datasets show that AHSTN achieves
better performance over several strong baselines.