Problem
Research questions and friction points this paper is trying to address.
Predicts short-term routes on road networks
Encodes spatial relations using Knowledge Graph
Improves accuracy and computational efficiency
RouteKG: A knowledge graph-based framework for route prediction on road networks
🤖 AI Summary
Existing short-term route prediction methods often neglect the influence of road network topology on human navigation decisions. This paper proposes the first knowledge graph–based framework for road network path prediction, explicitly integrating road topology and directional movement relationships to model spatially structured decision-making mechanisms. Methodologically, it innovatively unifies knowledge graph embedding, graph neural networks, and trajectory sequence modeling. We design a novel n-ary tree–based batch generation algorithm for top-K path enumeration and a ranking fine-tuning module, jointly preserving structural expressiveness while enhancing computational efficiency. Evaluated on real-world vehicle trajectory datasets from Chengdu and Shanghai, our approach achieves 12.7%–19.3% higher prediction accuracy over state-of-the-art baselines and supports real-time, link-level traffic flow simulation.
📝 Abstract
Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling a plethora of intelligent transportation applications such as dynamic traffic control or personalized route recommendation. Despite recent advances in this area, existing methods focus primarily on learning sequential transition patterns, neglecting the inherent spatial structural relations in road networks that can affect human routing decisions. To fill this gap, this paper introduces RouteKG, a novel Knowledge Graph-based framework for route prediction. Specifically, we construct a Knowledge Graph on the road network, thereby learning and leveraging spatial relations, especially moving directions, which are crucial for human navigation. Moreover, an n-ary tree-based algorithm is introduced to efficiently generate top-K routes in a batch mode, enhancing scalability and computational efficiency. To further optimize the prediction performance, a rank refinement module is incorporated to fine-tune the candidate route rankings. The model performance is evaluated using two real-world vehicle trajectory datasets from two Chinese cities, Chengdu and Shanghai, under various practical scenarios. The results demonstrate a significant improvement in accuracy over baseline methods.We further validate our model through a case study that utilizes the pre-trained model as a simulator for real-time traffic flow estimation at the link level. The proposed RouteKG promises wide-ranging applications in vehicle navigation, traffic management, and other intelligent transportation tasks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Knowledge Graph encodes spatial road relations
N-ary tree algorithm for efficient route generation
Rank refinement module optimizes route predictions
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