The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. Spatio-Temporal Graph Neural Networks (STGNNs) have been lauded for their adaptability to road graph structures. Yet, current research on STGNNs architectures often prioritizes complex designs, leading to elevated computational burdens with only minor enhancements in accuracy. To address this issue, we propose ST-MLP, a concise spatio-temporal model solely based on cascaded Multi-Layer Perceptron (MLP) modules and linear layers. Specifically, we incorporate temporal information, spatial information and predefined graph structure with a successful implementation of the channel-independence strategy - an effective technique in time series forecasting. Empirical results demonstrate that ST-MLP outperforms state-of-the-art STGNNs and other models in terms of accuracy and computational efficiency. Our finding encourages further exploration of more concise and effective neural network architectures in the field of traffic forecasting.

翻译：及时且精确的交通预测在优化智能交通系统（ITS）中的交通流管理方面至关重要，这一关键性已引起学术界的广泛关注。时空图神经网络（STGNNs）因其对道路图结构的适应性而备受推崇。然而，当前关于STGNNs架构的研究通常优先考虑复杂设计，导致计算负担加重，而准确性仅获得微小提升。为解决这一问题，我们提出ST-MLP，一种仅基于级联多层感知机（MLP）模块和线性层的简洁时空模型。具体而言，我们通过成功实施通道独立策略（一种时间序列预测中的有效技术），融合了时间信息、空间信息和预定义图结构。实验结果表明，ST-MLP在准确性和计算效率方面均优于最先进的STGNNs及其他模型。我们的发现鼓励在交通预测领域进一步探索更简洁、更高效的神经网络架构。