MemoClip: A Location-Based Remembrance Appliance
Personal and Ubiquitous Computing
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Because I carry my cell phone anyway: functional location-based reminder applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effective Density Queries on ContinuouslyMoving Objects
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied 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
Similarity-based prediction of travel times for vehicles traveling on known routes
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Extracting high-level information from location data: the W4 diary example
Mobile Networks and Applications
Going my way: a user-aware route planner
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mobility profiler: A framework for discovering mobility profiles of cell phone users
Pervasive and Mobile Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Discovering personally semantic places from GPS trajectories
Proceedings of the 21st ACM international conference on Information and knowledge management
Mining user similarity based on routine activities
Information Sciences: an International Journal
Context-awareness in the car: prediction, evaluation and usage of route trajectories
DESRIST'13 Proceedings of the 8th international conference on Design Science at the Intersection of Physical and Virtual Design
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Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward-Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin-destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80% and 60% accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user's historical movement data and predict the destination and future route.