STHMoE：基于LLM的城市交通数据预测的超图增强异构依赖协调

中文摘要 AI 辅助

时空交通预测是智能交通系统的一项基础大数据分析任务，其中海量城市传感器流表现出异构、非平稳和结构动态的模式。尽管近期基于深度学习和大语言模型（LLM）的方法推进了交通预测，但它们往往以时间为中心，并且在不断变化的交通状态下缺乏对时间、频谱、成对空间和高阶结构线索的有效协调。为解决这一异构依赖协调问题，我们提出了STHMoE，一种用于城市交通数据预测的时空超图增强混合专家框架。STHMoE将交通动态解耦为频域、时域、空间域和高阶空间表示，这些表示由基于部分冻结的LLM骨干构建的提示引导的异构专家建模。前三个专家利用特定领域的统计提示，而高阶空间专家使用结构占位符提示，并从自适应超图模块获取依赖信息。为捕捉交通数据中不断演变的空间结构，STHMoE在无需预定义拓扑的情况下联合学习一阶图依赖和高阶组交互。一个具有变异系数负载均衡的熵感知MoE路由器自适应地融合专家输出，同时提高专家利用率和路由置信度。在10个真实世界交通基准上的实验表明，STHMoE在时间、时空图及基于LLM的基线中取得了有竞争力的性能。

英文摘要

Spatio-temporal traffic forecasting is a fundamental big data analytics task for intelligent transportation systems, where massive urban sensor streams exhibit heterogeneous, non-stationary, and structurally dynamic patterns. Although recent deep learning and large language model (LLM)-based methods have advanced traffic forecasting, they often remain temporally centered and lack effective coordination of temporal, spectral, pairwise spatial, and higher-order structural cues under evolving traffic regimes. To address this heterogeneous dependency coordination problem, we propose STHMoE, a Spatio-Temporal Hypergraph-Enhanced Mixture of Experts framework for urban traffic data forecasting. STHMoE decouples traffic dynamics into frequency-domain, time-domain, spatio-domain, and higher-order spatial representations, which are modeled by prompt-guided heterogeneous experts built upon a partially frozen LLM backbone. The first three experts leverage domain-specific statistical prompts, while the higher-order spatio expert uses a structural placeholder prompt and obtains dependency information from an adaptive hypergraph module. To capture evolving spatial structures in traffic data,, STHMoE jointly learns first-order graph dependencies and higher-order group interactions without predefined topologies. An entropy-aware MoE router with coefficient-of-variation load balancing adaptively fuses expert outputs while improving expert utilization and routing confidence. Experiments on 10 real-world traffic benchmarks show that STHMoE achieves competitive performance against temporal, spatio-temporal graph, and LLM-based baselines.

发表机构

• Southeast University（东南大学）

机构由 AI 辅助整理，请以论文原文为准。