A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting

• URL: http://arxiv.org/abs/2609.13878v1
• Date: Sat, 12 Sep 2026 11:04:04 GMT
• Title: A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting
• Abstract summary: Existing pre-trained models often rely on a homogeneous modeling paradigm to handle highly heterogeneous traffic data.<n>We propose FlexST, a novel pre-training framework that introduces modularity and adaptivity for traffic modeling.<n>Experiments on 23 real-world traffic datasets demonstrate that FlexST significantly outperforms state-of-the-art baselines in zero- and few-shot settings.
• Score: 27.679556042019076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale modeling. Existing pre-trained models often rely on a homogeneous modeling paradigm to handle highly heterogeneous traffic data. This fundamental mismatch not only limits model generalization but also leads to computationally expensive and parameter-inefficient designs. To this end, we propose FlexST, a novel pre-training framework that introduces modularity and adaptivity for traffic modeling. Specifically, we first propose a multi-resolution spatio-temporal diffusion module that captures both short-term fluctuations and long-range trends, effectively reconciling inputs with divergent temporal and spatial resolutions. After that, we construct a domain-adaptive mixture-of-experts that dynamically routes data to specialized sub-networks, enabling selective knowledge transfer while preventing negative interference across diverse domains. Moreover, we devise a unified periodic encoding strategy that injects resolution- and domain-aware inductive biases to harmonize periodic inconsistencies across datasets. Extensive experiments on 23 real-world traffic datasets demonstrate that FlexST significantly outperforms state-of-the-art baselines in zero- and few-shot settings, showcasing superior generalization, adaptability and efficiency. This work offers a new direction for building general-purpose pre-trained models capable of handling the complexity and variability of urban traffic systems.

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