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.

使用时空图神经网络 (ST-GNN) 对实时交通数据建模，通过知识蒸馏 (KD) 提高 ST-GNN 在实时交通预测中的执行效率，使用剪枝算法和联合优化实现网络架构搜索和精调，评估结果表明，即使保留网络参数的仅 3％，我们的方法仍能使学生网络接近教师网络的准确性。

时空图卷积网络上的知识蒸馏用于交通预测

Knowledge Distillation on Spatial-Temporal Graph Convolutional Network  for Traffic Prediction

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor
datasets is an important problem for intelligent transportation systems and
sustainability. However, it is challenging due to the complex topological
dependencies and high dynamics associated with changing road conditions. In
this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to
address these challenges. In particular, given a series of road network
snapshots, we learn the attributes of vertices in latent spaces which capture
both topological and temporal properties. As these latent attributes are
time-dependent, they can estimate how traffic patterns form and evolve. In
addition, we present an incremental online algorithm which sequentially and
adaptively learn the latent attributes from the temporal graph changes. Our
framework enables real-time traffic prediction by 1) exploiting real-time
sensor readings to adjust/update the existing latent spaces, and 2) training as
data arrives and making predictions on-the-fly with given data. By conducting
extensive experiments with a large volume of real-world traffic sensor data, we
demonstrate the utility superiority of our framework for real-time traffic
prediction on large road networks over competitors as well as a baseline
graph-based LSM.

本文提出了一种基于潜在空间模型的道路网络（LSM-RN）用于解决实时交通预测的问题，该模型能够学习路网快照的属性，并根据时间依赖性进行实时交通预测，通过与大量真实交通传感器数据的实验比较也展示了其优越性。