全频谱预测：高吞吐量推理机制下的整体多粒度交通建模
Forecasting at Full Spectrum: Holistic Multi-Granular Traffic Modeling under High-Throughput Inference Regimes
AI总结：
针对现有GCN无法充分融合多粒度特征且推理慢的问题，提出雾分布式推理系统MultiGran-STGCNFog及调度算法GA-DPHDS，实现高效多粒度时空特征融合并显著提升推理吞吐量。
摘要：
值得注意的是，当前的智能交通系统严重依赖准确的交通预测和快速的推理提供来做出及时决策。尽管图卷积网络（GCNs）在建模复杂交通依赖关系方面已显示出优势，但现有的基于GCN的方法无法以完整方式充分提取和融合各种时空尺度上的多粒度时空特征，这被证明会产生不够准确的结果。此外，由于跨尺度提取多粒度特征在计算机视觉、自然语言处理和时间序列预测等领域一直是一种有前景的策略，开创性研究已尝试利用类似机制进行时空交通数据挖掘。然而，先前研究中引入的额外特征提取分支严重增加了模型复杂性并延长了推理时间，使得提供快速预测变得具有挑战性。在本文中，我们提出了MultiGran-STGCNFog，这是一种高效的雾分布式推理系统，采用一种新型交通预测模型，在生成的动态交通图上采用多粒度时空特征融合，以充分捕获相互依赖的交通动态。所提出的调度算法GA-DPHDS，同时优化层执行顺序和层设备调度方案，通过以流水线方式协调异构雾设备，为推理吞吐量的显著提升做出了贡献。在真实世界数据集上的大量实验证明了所提出方法优于所选GCN基线。
英文摘要：
Notably, current intelligent transportation systems rely heavily on accurate traffic forecasting and swift inference provision to make timely decisions. While Graph Convolutional Networks (GCNs) have shown benefits in modeling complex traffic dependencies, the existing GCN-based approaches cannot fully extract and fuse multi-granular spatiotemporal features across various spatial and temporal scales sufficiently in a complete manner, proven to yield less accurate results. Besides, as extracting multi-granular features across scales has been a promising strategy across domains such as computer vision, natural language processing, and time-series forecasting, pioneering studies have attempted to leverage a similar mechanism for spatiotemporal traffic data mining. However, additional feature extraction branches introduced in prior studies critically increased model complexity and extended inference time, making it challenging to provide fast forecasts. In this paper, we propose MultiGran-STGCNFog, an efficient fog distributed inference system with a novel traffic forecasting model that employs multi-granular spatiotemporal feature fusion on generated dynamic traffic graphs to fully capture interdependent traffic dynamics. The proposed scheduling algorithm GA-DPHDS, optimizing layer execution order and layer-device scheduling scheme simultaneously, contributes to considerable inference throughput improvement by coordinating heterogeneous fog devices in a pipelined manner. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method over selected GCN baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
