High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.
翻译:高性能交通流量预测模型是智能运输系统的核心技术,它的设计是工业和学术界的一项长期但依然具有挑战性的任务。物理原理和数据驱动模型之间缺乏整合是限制这一领域发展的一个重要原因。在文献中,物理基础方法通常可以对交通流量系统的动态过程提供清晰的解释,但准确性有限,而数据驱动方法,特别是黑箱结构的深层学习,可以提高性能,但由于缺乏合理的物理基础而不能完全得到信任。为了缩小纯数据驱动和物理驱动方法之间的差距,我们建议采用物理引导的物理定位深度学习模型,即Spatio-时间差异分布网络(STDEN),将交通流量动态的物理机制引入一个深入的神经网络框架。具体地说,我们假定公路网络上的交通流量是由潜在能源领域(如重力场驱动的水流)驱动的,并且由于缺乏合理的物理模型化模型化,将潜在能源领域的空间差异变化动态动态动态动态进程作为差异方程式网络的模型。STDEN在北京的物理模型模型和模型模型化的精确性模型模型中,可以展示一个具有重要性模型模型化的模型模型模型模型模型化的模型模型模型和数据模型化的模型化模型化模型化的模型模型化数据模型。