MobiDict : a mobility prediction system leveraging realtime location data streams

Détails

ID Serval
serval:BIB_27346589EF54
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
MobiDict : a mobility prediction system leveraging realtime location data streams
Titre de la conférence
Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming - IWGS '16
Auteur⸱e⸱s
Kulkarni V., Moro A., Garbinato B.
Editeur
ACM Press
ISBN
9781450345798
Statut éditorial
Publié
Date de publication
2016
Pages
53-62
Langue
anglais
Résumé
Mobility prediction is becoming one of the key elements of location-based services. In the near future, it will also facilitate tasks such as resource management, logistics administration and urban planning. To predict human mobility, many techniques have been proposed. However, existing techniques are usually driven by large volumes of data to train user mobility models computed over a long duration and stored in a centralized server. This results in inherently long waiting times before the prediction model kicks in. Over this large training data, small time bounded user movements are shadowed, due to their marginality, thus impacting the granularity of predictions. Transferring highly sensitive location data to third party entities also exposes the user to several privacy risks. To address these issues, we propose MobiDict, a realtime mobility prediction system that is constantly adapting to the user mobility behaviour, by taking into account the movement periodicity and the evolution of frequently visited places. Compared to the existing training approaches, our system utilises less data to generate the evolving mobility models, which in turn lowers the computational complexity and enables implementation on handheld devices, thus preserving privacy. We test our system using mobility traces collected around Lake Geneva region from 168 users and demonstrate the performance of our approach by evaluating MobiDict with six different prediction techniques. We find a satisfactory prediction accuracy as compared to the baseline results obtained with 70% of the user dataset for majority of the users.
Mots-clé
Realtime Mobility Prediction, Mobility Behaviour, Location based Services
Web of science
Création de la notice
13/07/2017 13:53
Dernière modification de la notice
20/08/2019 13:06
Données d'usage