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
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
Mining Individual Life Pattern Based on Location History
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Learning time-based presence probabilities
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Getting closer: an empirical investigation of the proximity of user to their smart phones
Proceedings of the 13th international conference on Ubiquitous computing
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predictability of individuals' mobility with high-resolution positioning data
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Place Learning via Direct WiFi Fingerprint Clustering
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
How long are you staying?: predicting residence time from human mobility traces
Proceedings of the 19th annual international conference on Mobile computing & networking
Using unlabeled Wi-Fi scan data to discover occupancy patterns of private households
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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Several algorithms to predict the next place visited by a user have been proposed in the literature. The accuracy of these algorithms -- measured as the ratio of the number of correct predictions and the number of all computed predictions -- is typically very high. In this paper, we show that this good performance is due to the high predictability intrinsic in human mobility. We also show that most algorithms fail to correctly predict transitions, i.e. situations in which users move between different places. To this end, we analyze the performance of 18 prediction algorithms focusing on their ability to predict transitions. We run our analysis on a data set of mobility traces of 37 users collected over a period of 1.5 years. Our results show that even algorithms achieving an overall high accuracy are unable to reliably predict the next location of the user if this is different from the current one. Building upon our analysis we then present a novel next-place prediction algorithm that can both achieve high overall accuracy and reliably predict transitions. Our approach combines all the 18 algorithms considered in our analysis and achieves its good performance at the cost of a higher computational and memory overhead.