论文标题
时空点流程，并注意交通拥堵事件建模
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling
论文作者
论文摘要
我们提出了一个新颖的框架，用于建模道路网络上的交通拥堵活动。通过将来自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，使用多模式数据，我们旨在捕获两种类型的触发效果对拥塞事件。当前一个位置的交通拥堵可能会导致道路网络的未来拥塞，交通事故可能会导致交通拥堵。为了模拟事件对过去的非均匀时间依赖性，我们使用基于嵌入点过程的神经网络的新型基于注意力的机制。为了结合道路网络引起的定向空间依赖性，我们将“尾巴”模型从空间统计的背景下调整到交通网络设置。与合成数据和真实数据的最新方法相比，我们证明了方法的出色性能。
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.
