ButterMamba：基于巴特沃斯增强时空状态空间模型的高效交通流预测方法
ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction
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中文总结 AI 辅助
ButterMamba是基于SSMs的高效交通流预测框架，含巴特沃斯谱滤波模块与并行Mamba时空状态混合器，在三个公开数据集上精度优于SOTA模型且训练时间、内存占用显著降低。
中文摘要 AI 辅助
准确的交通流预测是智能交通系统的基础，在城市出行优化和智慧城市发展中发挥关键作用。结合时间序列预测的图神经网络（GNNs）已成为有前景的解决方案，但仍存在两个关键局限：（1）基于注意力的架构具有二次复杂度，阻碍其在大规模网络中的实时部署；（2）传感器数据中的高频噪声会显著降低预测可靠性，这些挑战在对计算效率和噪声鲁棒性要求极高的都市场景中尤为突出。为解决这些局限，本文提出ButterMamba，一种基于状态空间模型（SSMs）的新型高效框架。该框架包含两个关键组件：（1）巴特沃斯谱滤波模块，通过去除高频噪声对数据进行预处理，使模型能够聚焦于重要的潜在趋势；（2）时空状态混合器，采用并行Mamba架构高效捕捉道路网络中的长程时间依赖关系和复杂空间相关性。通过将噪声滤波与时空建模解耦，ButterMamba实现了卓越的预测精度，且具有线性计算复杂度。在三个公开数据集上的大量实验表明，ButterMamba不仅在预测精度上优于现有最先进模型，还大幅减少了训练时间和内存使用量。
英文摘要
Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smart city development. While Graph Neural Networks (GNNs) integrated with time series forecasting have emerged as promising solutions, two critical limitations persist: (1) the quadratic complexity of attention-based architectures hinders real-time deployment in large-scale networks, and (2) high-frequency noise in sensor data significantly degrades prediction reliability. These challenges are particularly acute in metropolitan scenarios where both computational efficiency and noise robustness are paramount. To address these limitations, we introduce \textbf{ButterMamba}, a novel and efficient framework based on State Space Models (SSMs). ButterMamba consists of two key components: (1) a Butterworth Spectral Filtering module that preprocesses the data by removing high-frequency noise, allowing the model to focus on significant underlying trends, and (2) a Spatial-Temporal State Mixer that uses a parallel Mamba architecture to efficiently capture both long-range temporal dependencies and complex spatial correlations across the road network. By decoupling noise filtering from spatial-temporal modeling, ButterMamba achieves superior predictive accuracy with linear computational complexity. Extensive experiments on three public datasets demonstrate that ButterMamba not only outperforms existing state-of-the-art models in terms of prediction accuracy but also considerably reduces training time and memory usage.
发表机构
- Institute of Physical Science and Information Technology, Anhui University（安徽大学物理与信息技术学院）
- School of Innovation and Entrepreneurship, Shandong University（山东大学创新创业学院）
- School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
