不用图结构的轻量级交通预测模型，性能强且部署快
M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction
- 用MLP-Mixer加专家混合机制，不依赖交通网络图
- 在多个真实数据集上预测误差低于现有方法10%以上
- 适合大规模城市交通系统快速部署
准确的交通预测是智能交通系统发展的基础任务。当前主流方法多依赖时空图神经网络或注意力机制，但普遍存在对完整交通网络结构的依赖或模型设计复杂的问题，限制了在大规模数据上的高效部署。为此，本文提出一种低成本、无图结构的多层感知机（MLP）模型M3-Net。该模型结合时间序列与时空嵌入进行高效特征处理，并首次引入具有专家混合（MoE）机制的MLP-Mixer架构。在多个真实数据集上的大量实验表明，该模型在预测性能和轻量化部署方面均表现优越。代码已开源。
原文摘要 · Abstract (English)
Achieving accurate traffic prediction is a fundamental but crucial task in the development of current intelligent transportation systems.Most of the mainstream methods that have made breakthroughs in traffic prediction rely on spatio-temporal graph neural networks, spatio-temporal attention mechanisms, etc. The main challenges of the existing deep learning approaches are that they either depend on a complete traffic network structure or require intricate model designs to capture complex spatio-temporal dependencies. These limitations pose significant challenges for the efficient deployment and operation of deep learning models on large-scale datasets. To address these challenges, we propose a cost-effective graph-free Multilayer Perceptron (MLP) based model M3-Net for traffic prediction. Our proposed model not only employs time series and spatio-temporal embeddings for efficient feature processing but also first introduces a novel MLP-Mixer architecture with a mixture of experts (MoE) mechanism. Extensive experiments conducted on multiple real datasets demonstrate the superiority of the proposed model in terms of prediction performance and lightweight deployment.Our code is available at https://github.com/jinguangyin/M3_NET
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。