Cost-effective and scalable traffic forecasting: A graph-free multi-view MLP architecture

Achieving accurate and scalable traffic prediction is a cornerstone of modern intelligent transportation systems. While the recent advent of Large Language Models (LLMs) has introduced remarkable zero-shot capabilities to urban computing, their immense parametric scale and exorbitant deployment costs fundamentally restrict their viability for real-time, large-scale traffic operations. Concurrently, mainstream spatio-temporal forecasting methods remain heavily tethered to explicit topological graph structures or computationally expensive attention mechanisms, which inherently introduce severe scalability bottlenecks. These limitations underscore a critical divergence in current research: an urgent necessity to develop highly efficient, lightweight predictive architectures capable of bridging the gap between theoretical accuracy and practical deployability.To address this deployment imperative, we propose the Spatio-Temporal Multi-view MLP Network (STMMN), a highly scalable, graph-free computational framework. Diverging from traditional graph-dependent paradigms, STMMN utilizes an adaptive spatial clustering multilayer perceptron (MLP) module to autonomously extract latent spatial homogeneity. This mechanism streamlines spatial dimensionality, effectively circumventing the rigidity of predefined topologies and drastically reducing learning complexity. Furthermore, we introduce a temporal multi-view aggregation module that seamlessly integrates distinct temporal branches, specifically capturing proximity, trend, and periodicity, to dynamically fuse multi-scale temporal dependencies without relying on exhaustive attention maps.Extensive experiments conducted on multiple real-world, large-scale datasets confirm that STMMN achieves state-of-the-art prediction accuracy while exhibiting exceptional computational efficiency. Ultimately, this research validates the immense practical value of lightweight, knowledge-aware models in overcoming the deployment bottlenecks of large-scale urban engineering operations.