Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks

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.

HetGL2R: Learning to Rank Critical Road Segments via Attributed Heterogeneous Graph Random Walks

Accurately identifying critical nodes with high spatial influence in road networks is essential for enhancing the efficiency of traffic management and urban planning. However, existing node importance ranking methods mainly rely on structural features and topological information, often overlooking critical factors such as origin-destination (OD) demand and route information. This limitation leaves considerable room for improvement in ranking accuracy. To address this issue, we propose HetGL2R, an attributed heterogeneous graph learning approach for ranking node importance in road networks. This method introduces a tripartite graph (trip graph) to model the structure of the road network, integrating OD demand, route choice, and various structural features of road segments. Based on the trip graph, we design an embedding method to learn node representations that reflect the spatial influence of road segments. The method consists of a heterogeneous random walk sampling algorithm (HetGWalk) and a Transformer encoder. HetGWalk constructs multiple attribute-guided graphs based on the trip graph to enrich the diversity of semantic associations between nodes. It then applies a joint random walk mechanism to convert both topological structures and node attributes into sequences, enabling the encoder to capture spatial dependencies more effectively among road segments. Finally, a listwise ranking strategy is employed to evaluate node importance. To validate the performance of our method, we construct two synthetic datasets using SUMO based on simulated road networks. Experimental results demonstrate that HetGL2R significantly outperforms baselines in incorporating OD demand and route choice information, achieving more accurate and robust node ranking. Furthermore, we conduct a case study using real-world taxi trajectory data from Beijing, further verifying the practicality of the proposed method.