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.

翻译：交通状态预测是智能交通系统中的基础任务。实际应用中，部分区域因感知基础设施不足导致交通观测数据有限，这使得跨域知识迁移成为解决数据稀缺场景下交通预测问题的重要途径。然而，现有跨域交通预测方法仍存在若干局限，包括粗粒度的源-目标域适配、处理未见目标域模式的能力有限，以及对不规则或异质时间条件下连续交通动态建模不足。针对这些问题，本文提出一种连续跨域交通预测框架——记忆增强图液态时间常数网络（MA-GLTC）。具体而言，我们首先构建时空单元（STU）将交通网络分解为可迁移的局部单元，从而实现跨域细粒度知识对齐。其次，开发图液态时间常数网络（GLTC）对连续时间下的图耦合交通演化过程进行建模。与通用图神经ODE模型不同，GLTC将图耦合递归电导引入液态时间常数动力学，使节点状态能够伴随泄露、自适应时间常数及邻域感知反馈进行演化。此外，我们设计了基于记忆的迁移存储（MTS）机制，用于保留源域知识、检索匹配的交通模式，并在出现未见状态时更新可靠的目标域模式。在五个公开交通数据集上的实验表明，MA-GLTC在短期与长期预测任务中均稳定优于代表性域内及跨域基线方法。与次优方法相比，MA-GLTC平均预测误差分别降低3.02%、0.33%、8.92%、10.09%和2.11%。