Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F320

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 91–99 (2019)

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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, the mobility data can predict and generate routes of objects from the given past trends. In this work, the 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 extract and train geopositionig information. A sequence of longitude and latitude can be geographically mapped and such images 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.

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