Traffic prediction is essential for the progression of Intelligent
Transportation Systems (ITS) and the vision of smart cities. While
Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this
domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs
or Transformers, they present challenges such as computational complexity,
gradient issues, and resource-intensiveness. This paper addresses these
challenges, advocating for three main solutions: a node-embedding approach,
time series decomposition, and periodicity learning. We introduce STLinear, a
minimalist model architecture designed for optimized efficiency and
performance. Unlike traditional STGNNs, STlinear operates fully locally,
avoiding inter-node data exchanges, and relies exclusively on linear layers,
drastically cutting computational demands. Our empirical studies on real-world
datasets confirm STLinear's prowess, matching or exceeding the accuracy of
leading STGNNs, but with significantly reduced complexity and computation
overhead (more than 95% reduction in MACs per epoch compared to
state-of-the-art STGNN baseline published in 2023). In summary, STLinear
emerges as a potent, efficient alternative to conventional STGNNs, with
profound implications for the future of ITS and smart city initiatives.

STLinear 是一种能够优化智能交通系统和智慧城市愿景的交通预测模型，通过节点嵌入方法、时间序列分解和周期性学习解决了空间 - 时间图神经网络的计算复杂度、梯度问题和资源消耗性等挑战。通过实证研究，STLinear 在真实世界数据集上表现出色，不仅匹配或超过领先的空间 - 时间图神经网络的准确性，而且大幅减少了计算复杂度和计算开销（每个周期的 MACs 相比于 2023 年最先进的空间 - 时间图神经网络基线减少了 95% 以上），是传统模型的有效替代方案，对智能交通系统和智慧城市倡议的未来有深远的影响。