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.

为了优化智能交通系统（ITS）中的交通流管理，及时准确的交通预测的关键性已引起了大量学术关注。在道路图结构中，时空图神经网络（STGNNs）因其适应性而受到赞赏。然而，目前关于 STGNNs 架构的研究往往更偏向于复杂设计，从而导致计算负担增加，但提高的准确性不明显。为了解决这个问题，我们提出了 ST-MLP，一种基于级联多层感知机（MLP）模块和线性层的简洁时空模型。具体来说，我们结合了时间信息、空间信息和预定义的图结构，并成功实现了通道独立策略 —— 这是一种在时间序列预测中有效的技术。实证结果表明，ST-MLP 在准确性和计算效率方面优于最先进的 STGNNs 和其他模型。我们的发现鼓励在交通预测领域进一步探索更简洁和有效的神经网络架构。