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.

提出了一种名为 STGNPP 的时空图神经点过程框架，用于交通拥堵事件预测，通过设计时空图学习模块捕捉历史交通状态数据及道路网络的远程时空依赖关系，将提取的时空隐藏表示和拥堵事件信息输入到连续门循环单元中以建模拥堵演变模式，并通过周期门控机制改进点过程的强度函数计算以充分利用周期性信息，此模型同时预测下一个拥堵的发生时间和持续时间，在两个真实世界数据集上的大量实验表明，我们的方法在性能上优于现有的最先进方法。

时空图神经点过程用于交通拥堵事件预测

Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event  Prediction

Point process models are widely used to analyze asynchronous events occurring
within a graph that reflect how different types of events influence one
another. Predicting future events' times and types is a crucial task, and the
size and topology of the graph add to the challenge of the problem. Recent
neural point process models unveil the possibility of capturing intricate
inter-event-category dependencies. However, such methods utilize an unfiltered
history of events, including all event categories in the intensity computation
for each target event type. In this work, we propose a graph point process
method where event interactions occur based on a latent graph topology. The
corresponding undirected graph has nodes representing event categories and
edges indicating potential contribution relationships. We then develop a novel
deep graph kernel to characterize the triggering and inhibiting effects between
events. The intrinsic influence structures are incorporated via the graph
neural network (GNN) model used to represent the learnable kernel. The
computational efficiency of the GNN approach allows our model to scale to large
graphs. Comprehensive experiments on synthetic and real-world data show the
superior performance of our approach against the state-of-the-art methods in
predicting future events and uncovering the relational structure among data.

本研究提出了基于图点过程的方法来预测未来的事件，并利用图神经网络模型来表示可学习核，实现了对事件之间的关系结构的揭示，该方法在预测未来事件和展示数据关系结构方面优于现有方法。

深度图核点过程

Deep graph kernel point processes

We study the problem of learning Granger causality between event types from
asynchronous, interdependent, multi-type event sequences. Existing work suffers
from either limited model flexibility or poor model explainability and thus
fails to uncover Granger causality across a wide variety of event sequences
with diverse event interdependency. To address these weaknesses, we propose
CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework
for the studied task. The key idea of CAUSE is to first implicitly capture the
underlying event interdependency by fitting a neural point process, and then
extract from the process a Granger causality statistic using an axiomatic
attribution method. Across multiple datasets riddled with diverse event
interdependency, we demonstrate that CAUSE achieves superior performance on
correctly inferring the inter-type Granger causality over a range of
state-of-the-art methods.

CAUSE 是一个新颖的框架，通过拟合神经点过程来隐式捕捉事件互依存关系，然后使用公理归因方法从过程中提取格兰杰因果统计量，以解决学习异步、相互依赖、多类型事件序列的格兰杰因果关系的问题。在多个数据集上，CAUSE 表现优于一系列最先进的方法，能够正确推断一系列事件类型之间的格兰杰因果关系，包括在具有不同事件互依存关系的数据集上。