English(EN) ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction

ButterMamba框架通过噪声过滤和Mamba架构增强交通预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出ButterMamba，一个新颖的框架，旨在实现高效准确的交通流预测。该模型集成了Butterworth频谱滤波模块，用于去除传感器数据中的高频噪声，以及一个利用并行Mamba架构的空间-时间状态混合器，用于捕捉时空依赖性。ButterMamba实现了线性计算复杂度，在预测精度上优于现有最先进模型，同时减少了训练时间和内存使用。 AI

影响这项研究提供了一种更高效、更准确的交通流预测方法，有望改善城市交通和智慧城市发展。

排序理由该集群描述了一篇详细介绍特定应用新模型架构的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Limiao Zhang, Yuhui Lu, Jie Gao, Hao Jiang, Haiping Ma, Xingyi Zhang · 2026-09-01 04:00

ButterMamba：基于Butterworth增强的时空Mamba用于高效交通流预测

arXiv:2608.29658v1 Announce Type: cross 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 serie…

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报道来源 [1]

ButterMamba：基于Butterworth增强的时空Mamba用于高效交通流预测

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