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.

翻译：交通拥堵事件预测是智能交通系统中一项重要且具有挑战性的任务。现有许多交通预测工作整合了各类时间编码器与图卷积网络（GCN），形成所谓的时空图神经网络，其重点在于预测时间快照中的流量、速度和需求等密集变量，但难以预测在连续时间轴上稀疏分布的交通拥堵事件。近年来，神经点过程（NPP）已成为连续时间场景下事件预测的合适框架。然而，多数传统NPP方法无法模拟复杂的时空依赖关系与拥堵演化模式。为解决这些局限，我们提出一种名为STGNPP的时空图神经点过程框架，用于交通拥堵事件预测。具体而言，我们首先设计时空图学习模块，从历史交通状态数据及道路网络中充分捕获长程时空依赖关系。随后将提取的时空隐表示与拥堵事件信息输入连续门控循环单元，以建模拥堵演化模式。特别地，为充分利用周期性信息，我们还通过周期性门控机制改进了点过程的强度函数计算。最终，模型同步预测下一次拥堵的发生时间与持续时间。在两个真实数据集上的大量实验表明，与现有最先进方法相比，我们的方法取得了更优性能。