Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling
Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling
复制标题
交通拥堵事件建模的时空点过程研究
DOI：
10.1109/tits.2021.3068139
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发表时间：
2020-05
影响因子：
8.5
通讯作者：
Shixiang Zhu;Ruyi Ding;Minghe Zhang;P. V. Hentenryck;Yao Xie
中科院分区：
文献类型：
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作者：
Shixiang Zhu;Ruyi Ding;Minghe Zhang;P. V. Hentenryck;Yao Xie
我们提出了一个新的框架，道路网络上的交通拥堵事件建模。通过将交通传感器的计数数据与报告交通事件的警察报告相结合，使用多模态数据，我们的目标是捕获两种类型的拥堵事件的触发效应。一个地点当前的交通拥堵可能会导致道路网络未来的拥堵，而交通事故可能会导致交通拥堵的蔓延。为了模拟事件对过去的非均匀时间依赖性，我们使用了一种基于神经网络嵌入点过程的新的基于注意力的机制。为了将道路网络引起的方向性空间依赖性，我们将空间统计背景下的“尾部”模型应用于交通网络设置。我们证明了我们的方法的上级性能相比，国家的最先进的合成和真实的数据的方法。
We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.