Year-end Sale % OFF 
Abstract
Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
