Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
arXiv:2606.15807v1 Announce Type: cross Abstract: 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
This paper leverages recent advancements in graph neural networks and memory-augmented architectures to address persistent challenges in real-world traffic prediction, particularly in data-scarce environments.
Improving the accuracy and robustness of traffic prediction in varied conditions can enhance efficiency, reduce congestion, and inform urban planning, directly impacting logistics and smart city infrastructure.
The proposed method offers a more granular and adaptive approach to cross-domain knowledge transfer, potentially leading to more effective traffic management systems even with limited local data.
- · Intelligent Transportation Systems (ITS) developers
- · Urban planners
- · Logistics companies
- · Smart city initiatives
- · Traditional traffic modeling methods
- · Regions with insufficient sensing infrastructure without cross-domain solutions
More efficient and resilient urban mobility systems emerge, reducing economic costs associated with congestion.
The ability to manage traffic flow dynamically could reduce fuel consumption and carbon emissions in urban areas.
Enhanced traffic predictability might enable new forms of on-demand transportation services and autonomous vehicle integration pathways.
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Read at arXiv cs.AI