M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction

• URL: http://arxiv.org/abs/2508.08543v2
• Date: Thu, 14 Aug 2025 01:45:48 GMT
• Title: M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction
• Authors: Guangyin Jin, Sicong Lai, Xiaoshuai Hao, Mingtao Zhang, Jinlei Zhang,
• Abstract summary: We propose a cost-effective graph-free Multilayer Perceptron (M3-MLP) based model for traffic prediction.<n>Extensive experiments conducted on multiple real datasets demonstrate the superiority of the proposed model in terms of prediction performance.
• Score: 2.5130822801965738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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