Generating synthetic mobility traffic using RNNs

Détails

ID Serval
serval:BIB_620CB44CAAFD
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Generating synthetic mobility traffic using RNNs
Titre de la conférence
Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery - GeoAI '17
Auteur⸱e⸱s
Kulkarni V., Garbinato B.
Editeur
ACM Press
Adresse
Los Angeles, US
ISBN
9781450354981
Statut éditorial
Publié
Date de publication
2017
Pages
1-4
Langue
anglais
Résumé
Mobility trajectory datasets are fundamental for system evaluation and experimental reproducibility. Privacy concerns today however, have restricted sharing of such datasets. This has led to the development of synthetic traffic generators, which simulate moving entities to create pseudo-realistic trajectory datasets. Existing work on traffic generation, superficially matches a-priori modeled mobility characteristics, which lacks realism and does not capture the substantive properties of human mobility. Critical applications however, require data that contains these complex, candid and hidden mobility patterns. To this end, we investigate the effectiveness of Recurrent Neural Networks (RNN) to learn these hidden patterns contained in an original dataset to produce a realistic synthetic dataset. We observe that, the ability of RNNs to learn and model problems over sequential data having long-term temporal dependencies is ideal for capturing the inherent properties of location traces. Additionally, the lack of intuitive high-level spatiotemporal structure and instability, guarantees trajectories that are different from the ones seen in the training dataset. Our preliminary evaluation results show that, our model effectively captures the sleep cycles and stay-points commonly observed in the considered training dataset, along with preserving the statistical characteristics and probability distributions of the movement transitions. Although, many questions remain to be answered, we show that generating synthetic traffic by learning the innate structure of human mobility through RNNs is a promising approach.
Création de la notice
29/11/2017 14:31
Dernière modification de la notice
21/08/2019 5:17
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