@inproceedings{74725833062b46ac9fd33d5df5129e6a,
title = "Sensor-Based Approach for Predicting Departure Time of Smartphone Users",
abstract = "While location prediction of smartphone users has made great strides in recent years, a major challenge remains. As users spend the majority of their time is several fixed locations (home, work), existing algorithms are unable to identify the exact time in which a person is likely to depart from one place to another. In this work we present a sensor-based approach designed to predict the departure time of users. By using location and accelerometer sensors we were able to train a generic classification model that is able to predict whether the user will stay put or move to a different location with true positive rate of 0.73 and false positive rate of 0.3.",
keywords = "Location Prediction, Machine Learning",
author = "Ron Biton and Gilad Katz and Asaf Shabtai",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015 ; Conference date: 16-05-2015 Through 17-05-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/MobileSoft.2015.37",
language = "English",
series = "Proceedings - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "146--147",
booktitle = "Proceedings - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015",
address = "United States",
}