Generating human mobility route based on generative adversarial network

Abstract

Recently, many researches on human mobility are aiming to suggest the personal customized solution in the diverse field, usually by academia and industry. Combined with deep learning methods, it is able to predict and generate novel routes of objects from the mobility data including the given past trends. In this work, Generative Adversarial Network (GAN) model is introduced for creating individual mobility routes based on sets of accumulated personal mobility data. The mobility data had been collected by use of geopositioning system and personal mobile devices. GAN has Discriminator and Generator which are composed of neural networks, and can train and extract geopositionig information. A sequence of longitude and latitude can be geographically mapped, and matrices including all these information can be handled by GAN. The GAN-based model successfully handled individual mobility routes in this way. Consequently, our model can generate and suggest unexplored routes from the existing sets of personal geolocation data.