  Traffic congestion event prediction is an important yet challenging task in
intelligent transportation systems. Many existing works about traffic
prediction integrate various temporal encoders and graph convolution networks
(GCNs), called spatio-temporal graph-based neural networks, which focus on
predicting dense variables such as flow, speed and demand in time snapshots,
but they can hardly forecast the traffic congestion events that are sparsely
distributed on the continuous time axis. In recent years, neural point process
(NPP) has emerged as an appropriate framework for event prediction in
continuous time scenarios. However, most conventional works about NPP cannot
model the complex spatio-temporal dependencies and congestion evolution
patterns. To address these limitations, we propose a spatio-temporal graph
neural point process framework, named STGNPP for traffic congestion event
prediction. Specifically, we first design the spatio-temporal graph learning
module to fully capture the long-range spatio-temporal dependencies from the
historical traffic state data along with the road network. The extracted
spatio-temporal hidden representation and congestion event information are then
fed into a continuous gated recurrent unit to model the congestion evolution
patterns. In particular, to fully exploit the periodic information, we also
improve the intensity function calculation of the point process with a periodic
gated mechanism. Finally, our model simultaneously predicts the occurrence time
and duration of the next congestion. Extensive experiments on two real-world
datasets demonstrate that our method achieves superior performance in
comparison to existing state-of-the-art approaches.
