高效交通状态预测与动态联合时空关系推理
Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference
AI总结：
提出STEI-PCN纯卷积网络，通过局部因果联合时空图、位置编码和动态边权推理，高效捕获时空依赖，实现多步交通预测，在多个数据集上达到竞争精度且计算成本低。
摘要：
由于时间演化、空间交互以及道路网络上延迟的时空传播之间的复杂相互作用，交通预测具有挑战性。现有方法要么分别建模空间和时间依赖性，要么采用统一的时空结构，但它们往往不足以刻画历史时间戳上的相邻传感器如何影响目标节点，而复杂的联合模型可能带来高计算量。本文提出STEI-PCN，一种基于时空编码和关系推理的高效纯卷积网络。它首先构建局部因果联合时空图以限制候选交互，然后使用绝对位置和相对距离编码来推断动态边权。具有位置感知门控激活单元的单层图卷积捕获局部联合依赖，时间扩张因果卷积补充长程时间模式。多视图预测模块融合原始、局部传播和长程时间表示以进行直接多步预测。在PeMS03、PeMS04、PeMS07、PeMS08和PeMS-Bay上的多个预测时域实验表明，STEI-PCN以适中的参数和低训练推理成本实现了具有竞争力的精度。消融和波动分析进一步验证了主要组件的贡献，并实证分析了在速度急剧变化下训练阶段约束的影响。我们的代码可在GitHub链接获取。
英文摘要：
Traffic prediction is difficult due to the complex interplay of temporal evolution, spatial interactions, and delayed spatio-temporal propagation over road networks. Existing methods either model spatial and temporal dependencies separately or employ unified spatio-temporal structures, but they often insufficiently characterize how neighboring sensors at historical timestamps influence a target node, while complex joint models may incur high computation. This paper proposes STEI-PCN, an efficient pure convolutional network based on spatio-temporal encoding and relation inference. It first builds a local causal joint spatio-temporal graph to restrict candidate interactions, then uses absolute position and relative distance encodings to infer dynamic edge weights. A single-layer graph convolution with a position-aware gated activation unit captures local joint dependencies, and temporal dilated causal convolutions complement long-range temporal patterns. A multi-view prediction module fuses raw, local propagation, and long-range temporal representations for direct multi-step forecasting. Experiments on PeMS03, PeMS04, PeMS07, PeMS08, and PeMS-Bay under multiple horizons show that STEI-PCN achieves competitive accuracy with moderate parameters and low training and inference costs. Ablation and fluctuation analyses further verify the contributions of the main components and empirically analyze the effects of the training-stage constraints under sharp speed changes. Our code is available at a GitHub link https://github.com/Jessez2/STEI-PCN.
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。
