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Improving human mobility identification with trajectory augmentation / Fan Zhou in Geoinformatica, vol 25 n° 3 (July 2021)  

Public [article]  inGeoinformatica > vol 25 n° 3 (July 2021) . - pp 453 - 483 Titre : Improving human mobility identification with trajectory augmentation Type de document : Article/Communication Auteurs : Fan Zhou, Auteur ; Ruiyang Yin, Auteur ; Goce Trajcevski, Auteur ; Kunpeng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 453 - 483 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] itinéraire [Termes IGN] mobilité humaine [Termes IGN] modèle numérique de déplacement [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal convolutif [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Many location-based social networks (LBSNs) applications such as customized Point-Of-Interest (POI) recommendation, preference-based trip planning, travel time estimation, etc., involve an important task of understanding human trajectory patterns. In particular, identifying and linking trajectories to users who generate them – a problem called Trajectory-User Linking (TUL) – has become a focus of many recent works. TUL is usually studied as a multi-class classification problem and has gained recent attention because: (1) the number of labels/classes (i.e., users) is way larger than the number of motion patterns among various trajectories; and (2) the location-based trajectory data, especially the check-ins – i.e., events of reporting a location at particular Point of Interest (POI) with known semantics – are often extremely sparse. Towards addressing these challenges, we introduce a Trajectory Generative Adversarial Network (TGAN) as an approach to enable learning users motion patterns and location distribution, and to eventually identify human mobility. TGAN consists of two jointly trained neural networks, playing a Minimax game to (iteratively) optimize both components. The first one is the generator, learning trajectory representation by a Recurrent Neural Network (RNN) based model, aiming at fitting the underlying trajectory distribution of a particular individual and generate synthetic trajectories with intrinsic invariance and global coherence. The second one is the discriminator – a Convolutional Neural Network (CNN) based model that discriminates the generated trajectory from the real ones and provides guidance to train the generator model. We demonstrate that the above two models can be well tuned together to improve the TUL performance, while achieving superior accuracy when compared to existing approaches. Numéro de notice : A2021-972 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00378-7 Date de publication en ligne : 29/08/2019 En ligne : https://doi.org/10.1007/s10707-019-00378-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100390 [article]