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  1. Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '16)
  2. MobiDict: a mobility prediction system leveraging realtime location data streams
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Formalization of network-constrained moving object queries with application to benchmarking
Continuous detection of black holes for moving objects at sea
Categorizing spatiotemporal aggregates for moving regions
On computing temporal functions for time-dependent networks using trajectory data streams
ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
A survey of techniques and open-source tools for processing streams of spatio-temporal events
A general feature-based map matching framework with trajectory simplification
MobiDict: a mobility prediction system leveraging realtime location data streams
Mining frequent episodes from multivariate spatiotemporal event sequences
Mining frequent trajectory patterns from online footprints

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MobiDict: a mobility prediction system leveraging realtime location data streams

Content Provider ACM Digital Library
Author Moro, Arielle Kulkarni, Vaibhav Garbinato, BenoƮt
Abstract 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.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450345798
DOI 10.1145/3003421.3003424
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-31
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Mobility behaviour Realtime mobility prediction Location based services
Content Type Text
Resource Type Article
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