Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
How routine learners can support family coordination
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The self-programming thermostat: optimizing setback schedules based on home occupancy patterns
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Contextual conditional models for smartphone-based human mobility prediction
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
EarlyOff: using house cooling rates to save energy
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Energy-aware meeting scheduling algorithms for smart buildings
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Using unlabeled Wi-Fi scan data to discover occupancy patterns of private households
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Circulo: Saving Energy with Just-In-Time Hot Water Recirculation
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants' self-reported home/away schedules are not very accurate, and we introduce a probabilistic home/away schedule computed from observed GPS data. The computation includes smoothing and a soft schedule template. We show how the probabilistic schedule outperforms both the self-reported schedule and an algorithm based on driving time. We also show how to combine our algorithm with the best part of the drive time algorithm for a slight boost in performance.