  As a core technology of Intelligent Transportation System (ITS), traffic flow
prediction has a wide range of applications. Traffic flow data are
spatial-temporal, which are not only correlated to spatial locations in road
networks, but also vary with temporal time indices. Existing methods have
solved the challenges in traffic flow prediction partly, focusing on modeling
spatial-temporal dependencies effectively, while not all intrinsic properties
of traffic flow data are utilized fully. Besides, there are very few attempts
at incremental learning of spatial-temporal data mining, and few previous works
can be easily transferred to the traffic flow prediction task. Motivated by the
challenge of incremental learning methods for traffic flow prediction and the
underutilization of intrinsic properties of road networks, we propose a
Transport-Hub-aware Spatial-Temporal adaptive graph transFormer (H-STFormer)
for traffic flow prediction. Specifically, we first design a novel spatial
self-attention module to capture the dynamic spatial dependencies. Three graph
masking matrices are integrated into spatial self-attentions to highlight both
short- and long-term dependences. Additionally, we employ a temporal
self-attention module to detect dynamic temporal patterns in the traffic flow
data. Finally, we design an extra spatial-temporal knowledge distillation
module for incremental learning of traffic flow prediction tasks. Through
extensive experiments, we show the effectiveness of H-STFormer in normal and
incremental traffic flow prediction tasks. The code is available at
https://github.com/Fantasy-Shaw/H-STFormer.
