Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only $3\%$ of network parameters.

翻译：高效的实时交通预测对于减少交通时间至关重要。为预测交通状况，我们采用时空图神经网络（ST-GNN）将实时交通数据建模为时序图。尽管其具备强大能力，但在处理真实交通数据时仍常面临实现高效实时预测的挑战。鉴于实时数据的动态特性凸显了及时预测的重要性，我们采用知识蒸馏（KD）作为解决方案以提升ST-GNN在交通预测中的执行效率。本文提出一种成本函数，用于利用复杂网络（教师网络）蒸馏出的数据训练参数更少的网络（学生网络），同时使其精度接近教师网络。我们通过知识蒸馏，将教师网络中的时空相关性传递给学生网络，使其学习到教师感知的复杂模式。然而，如何确定学生网络架构而非随意设计成为挑战。为此，我们提出一种算法：利用成本函数计算剪枝分值，解决小型网络架构搜索问题，并联合使用KD技术对每个剪枝阶段生成的网络进行微调。最终，我们在两个真实数据集PeMSD7和PeMSD8上评估了所提方法。结果表明，即使仅保留$3\%$的网络参数，该方法仍能使学生的预测精度接近教师。