Problem
Research questions and friction points this paper is trying to address.
traffic flow prediction
graph neural networks
computational efficiency
noise robustness
real-time deployment
ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction
🤖 AI Summary
ButterMamba通过Butterworth滤波器去除高频噪声，并利用时空状态混合器高效捕捉时空依赖性，解决了交通流量预测中的计算效率和噪声鲁棒性问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Butterworth Spectral Filtering
Spatial-Temporal State Mixer
State Space Models (SSMs)
linear computational complexity
high-frequency noise
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