Simulation of Urban Landscape Around Subway Station Based on Machine Learning and Virtual Reality

Abstract

Abstract Assessment of living arrangement time, assumes a significant function in the activity of the metropolitan railroad framework. A scope of models dependent on AI and computer generated reality have been proposed. Limits the speculation execution of the suggestion AI technique, is temperamental precision of existing miniature reenactment model. Estimation model of the new residence time based on machine learning has been proposed. Possibility fundamental factors that affect the residence time of the urban railway has been first in the analysis. Based on the overall estimation model, the relationship extracted subsequently between different elements, it is modeled by machine learning, and it is proposed. Finally, an example of how a set of observation data from the subway have been proposed will be used in order to confirm its overall performance. Traditional landscape planning and design method of the city, efficient, intuitive, and collaborative planning the overall site planning, we will not be able to variety in the design of the program and the scene effect. Machine learning and graphics-based virtual reality (VR) technology, has been designed to play an important role in urban landscape planning. Results show that virtual reality technology exhibits significantly improved design efficiency and designer, by timely information to facilitate a realistic design quality of the processing of effective information, provides intelligent drawing the shows.