多分辨率多域预训练框架用于通用交通预测
A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting
另 2 家 · 查看机构详情
- National University of Defense Technology（国防科技大学）
- Shandong University（山东大学）
- HKUST (GZ)（香港科技大学（广州））
- Didichuxing Co. Ltd（滴滴出行科技有限公司）
- HKUST (GZ) and HKUST（香港科技大学（广州）和香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对交通数据异质性导致预训练模型泛化差的问题，提出FlexST框架，通过多分辨率扩散模块、域自适应混合专家和统一周期编码，在23个数据集上零样本和少样本设置中显著超越现有基线。
中文摘要 AI 辅助
时空交通数据是智能交通系统的核心，但其异质性给大规模建模带来了重大挑战。现有的预训练模型通常依赖同质建模范式来处理高度异质的交通数据。这种根本性的不匹配不仅限制了模型的泛化能力，还导致了计算成本高昂且参数效率低下的设计。为此，我们提出了FlexST，一种新颖的预训练框架，为交通建模引入了模块化和自适应性。具体而言，我们首先提出一个多分辨率时空扩散模块，该模块能够捕捉短期波动和长期趋势，有效协调具有不同时间和空间分辨率的输入。之后，我们构建了一个域自适应混合专家模型，将数据动态路由到专门的子网络，实现选择性知识迁移，同时防止跨不同领域的负面干扰。此外，我们设计了一种统一的周期编码策略，注入分辨率和领域感知的归纳偏置，以协调跨数据集的周期不一致性。在23个真实世界交通数据集上的大量实验表明，FlexST在零样本和少样本设置中显著优于最先进的基线，展示了卓越的泛化能力、适应性和效率。这项工作为构建能够处理城市交通系统复杂性和多变性的通用预训练模型提供了新方向。
英文摘要
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.
