Traffic density is a crucial indicator for evaluating the level of service, as it directly reflects the degree of road congestion and driving comfort. However, accurately predicting real-time traffic density has been a significant challenge in Intelligent Transportation Systems (ITS) due to the nonlinear and spatial-temporal dynamic complexity of traffic density. In this paper, we propose a novel Model-enhanced Spatial-Temporal Attention Network (MSTAN), which constructs a spatial-temporal traffic kernel density model using the Kernel Density Estimation (KDE) method to process the spatiotemporal data and calculate the probabilities of various spatiotemporal events. These probabilities are input into the attention mechanism, enabling the model to recognize the inherent connection between dynamic and distant events. Through this fusion, the network can deeply learn and analyze the spatial-temporal properties of traffic features. Furthermore, this paper utilizes the attention mechanism to dynamically model spatial-temporal dependencies, capturing real-time traffic conditions and density, and constructs a spatial-temporal attention module for learning. To validate the performance of the proposed MSTAN model, experiments are conducted on two public datasets of California highways (PeMS04 and PeMS08). The experimental results demonstrate that the MSTAN model outperforms existing state-of-the-art baseline models in terms of prediction accuracy, thus proving the effectiveness of the model both theoretically and practically.

交通密度是评价服务水平的重要指标，因为它直接反映了道路拥堵程度和驾驶舒适性。然而，由于交通密度的非线性和时空动态复杂性，准确预测实时交通密度一直是智能交通系统 （ITS） 面临的一项重大挑战。在本文中，我们提出了一种新的模型增强时空注意力网络 （MSTAN），它使用核密度估计 （KDE） 方法构建了一个时空交通核密度模型来处理时空数据并计算各种时空事件的概率。这些概率被输入到注意力机制中，使模型能够识别动态事件和远程事件之间的内在联系。通过这种融合，网络可以深入学习和分析交通特征的时空属性。此外，本文利用注意力机制对时空依赖性进行动态建模，捕捉实时交通状况和密度，并构建了一个用于学习的时空注意力模块。为了验证所提出的 MSTAN 模型的性能，在加利福尼亚高速公路的两个公共数据集（PeMS04 和 PeMS08）上进行了实验。实验结果表明，MSTAN 模型在预测精度方面优于现有的最先进的基线模型，从而从理论和实践上证明了该模型的有效性。