Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlying nonlinear function approximator has received comparatively less attention. In this work, we propose STKAN, a spatio-temporal forecasting architecture that introduces Taylor-polynomial Kolmogorov--Arnold Network modules into spatial and temporal token mixing. STKAN first constructs high-level spatial representations through a learnable soft node-group assignment mechanism, applies group-wise spatial mixing, and subsequently models temporal dependencies over the compressed sequence. Spatial and temporal self-attention layers are further employed to capture long-range interactions. Experiments on five traffic forecasting benchmarks show that STKAN achieves competitive performance and performs better than the evaluated MLP-based variant in the tested settings. These results suggest that the design of nonlinear function approximators can serve as a useful complement to architectural design in spatio-temporal forecasting.
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.
