提出新型模型，提升数据少区域的交通状态预测精度。
Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
- 将交通网络拆分为局部单元，实现跨域知识细粒度对齐。
- 引入图耦合时变动态建模，适应不规则时间下的连续交通演化。
- 内存机制可保留源域知识并自适应更新目标域未知模式，适合低观测场景。
交通状态预测是智能交通系统的基础任务。实际应用中，部分区域因感知基础设施不足导致数据稀缺，跨域知识迁移成为解决该问题的重要手段。然而现有方法仍存在源-目标域适配粗略、难以处理未见目标域模式、在非均匀或异构时间条件下对连续交通动态建模能力不足等问题。为此，本文提出一种连续跨域交通状态预测框架——记忆增强图液态时间常数网络（MA-GLTC）。首先构建时空单元（STUs），将交通网络分解为可迁移的局部单元，实现域间细粒度知识对齐。其次设计图液态时间常数网络（GLTC），以图耦合递归导纳建模图结构耦合的连续时间交通演化过程；不同于通用图神经微分方程模型，GLTC引入图耦合循环导纳，使节点状态具备泄漏性、自适应时间常数与邻域感知反馈。此外，设计基于记忆的迁移存储机制（MTS），用于保存源域知识、检索匹配的交通模式，并在出现未见状态时更新可靠的靶域模式。在五个公开交通数据集上的实验表明，MA-GLTC在短时与长时预测任务中均持续优于代表性单域与跨域基线方法。相比次优方法，平均预测误差分别降低3.02%、0.33%、8.92%、10.09%和2.11%。
原文摘要 · Abstract (English)
Traffic state prediction is a fundamental task in intelligent transportation systems. In practical applications, some regions suffer from limited traffic observations due to insufficient sensing infrastructure, making cross-domain knowledge transfer an important solution for data-scarce traffic prediction. However, existing cross-domain traffic prediction methods still face several limitations, including coarse-grained source-target adaptation, limited capability in handling unseen target-domain patterns, and insufficient modeling of continuous traffic dynamics under irregular or heterogeneous temporal conditions. To address these issues, this paper proposes a continuous cross-domain traffic prediction framework, termed Memory-Augmented Graph Liquid Time-Constant Network (MA-GLTC). Specifically, we first construct spatio-temporal units (STUs) to decompose traffic networks into transferable local units, enabling fine-grained knowledge alignment across domains. Then, a graph liquid time-constant network (GLTC) is developed to model graph-coupled traffic evolution in continuous time. Different from generic graph neural ODE-based models, GLTC introduces graph-coupled recurrent conductance into liquid time-constant dynamics, allowing node states to evolve with leakage, adaptive time constants, and neighborhood-aware feedback. Furthermore, a Memory-based Transfer Storage (MTS) mechanism is designed to preserve source-domain knowledge, retrieve matched traffic patterns, and update reliable target-domain patterns when unseen states emerge. Experiments on five public traffic datasets demonstrate that MA-GLTC consistently outperforms representative innerdomain and cross-domain baselines in both short-term and longterm prediction tasks. Compared with the second-best method, MA-GLTC reduces the average prediction errors by 3.02%, 0.33%, 8.92%, 10.09%, and 2.11%, respectively.
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