RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Estimate Shortest Path Distance Along Road Networks

Computing shortest distance estimation for road networks is an important component of map service systems. Existing embedded-based shortest path distance estimation methods either have a long training time or the model training time is reduced by sacrificing the estimation accuracy. To address the above problems, this paper proposes a Road Graph Convolutional Networks and Distance2Vector (RGCNdist2vec), which is suitable for road network scenarios, as an embedding method of road network vertices. Used to capture network structure information. In the aspect of sampling model training samples, a three-stage sampling method based on graph logical partition is designed, which can select a small number of high-quality samples for model training. In order to verify the validity of the model and sampling scheme, experiments were carried out on four real road network datasets and compared with existing relevant models. The results show that the proposed model has high estimation accuracy, and the training time of the model is nearly 4 times lower than that of the existing baseline model.