Generating human mobility route based on generative adversarial network

• Autorzy: Song Ha Yoon , Baek Moo Sang , Sung Minsuk

Identyfikatory

DOI

10.15439/2019F320

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data analysis; artificial intelligence; mobile computing; mobile handsets

PL

analiza danych; sztuczna inteligencja; komputer mobilny; telefon komórkowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 91--99
• Opis fizyczny: Bibliogr. 22 poz., wz., wykr., tab., rys.

Twórcy

autor

Song Ha Yoon

• Department of Computer Engineering, Hongik University, Seoul, Republic of Korea

autor

Baek Moo Sang

• Research Institute of Science and Technology, Hongik University, Seoul, Republic of Korea

autor

Sung Minsuk

• Department of Computer Engineering, Hongik University, Seoul, Republic of Korea

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).