  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.
