PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
A predictive location model for location-based services
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Mining traffic data from probe-car system for travel time prediction
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Your way your missions: from location-based to route-based pervasive gaming
Proceedings of the international conference on Advances in computer entertainment technology
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Easishop: Ambient intelligence assists everyday shopping
Information Sciences: an International Journal
Hiding Sensitive Trajectory Patterns
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Designing planned route based interactions for context-aware applications
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
A destination prediction method using driving contexts and trajectory for car navigation systems
Proceedings of the 2009 ACM symposium on Applied Computing
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
MEK: Using spatial-temporal information to improve social networks and knowledge dissemination
Information Sciences: an International Journal
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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
Information Sciences: an International Journal
Clustering and prediction of mobile user routes from cellular data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Processing generalized k-nearest neighbor queries on a wireless broadcast stream
Information Sciences: an International Journal
Mining regular routes from GPS data for ridesharing recommendations
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Mining user similarity based on routine activities
Information Sciences: an International Journal
Self-configuring data mining for ubiquitous computing
Information Sciences: an International Journal
Mining driving preferences in multi-cost networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client-server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method.