Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Evaluating location predictors with extensive Wi-Fi mobility data
ACM SIGMOBILE Mobile Computing and Communications Review
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
Crossing over the bounded domain: from exponential to power-law inter-meeting time in MANET
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Examining micro-payments for participatory sensing data collections
Proceedings of the 12th ACM international conference on Ubiquitous computing
Dynamic pricing incentive for participatory sensing
Pervasive and Mobile Computing
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
The Impact of Temporal Factors on Mobility Patterns
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and user adaptation in location forecasting
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Predicting the location of a mobile user in the near future can be used for a very large number of user-centered or crowd-centered ubiquitous applications. It is convenient for the discussion to think in terms of discrete locations driven by Points of Interest (POI) instead of absolute positions. We postulate that POI sequences are Markovian once the data is clustered by day of the week and time of the day. To prove our hypothesis we used a public dataset, used in a previous work [16]. In that paper the authors were able to predict the location of a user with 90% to 70% accuracy in five minutes and one hour time windows, respectively. With our approach, using Hidden Markov Models, we are able to predict the next POIs within seven hours without significant accuracy decrease. This result enables a large number of potential applications where the aggregate data of a single users conform the behavior of the crowd.