A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Haze: privacy-preserving real-time traffic statistics
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Improving route prediction through user journey detection
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Moving object data, in particular of mobile users, is becoming widely available. A GPS trajectory of a moving object is a time-stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for a range of applications. Existing studies have extracted knowledge from trajectory patterns for both single and multiple GPS trajectories. However, few works have taken into account the unreliability of GPS measurements for mobile devices or focused on the extraction of fine-grained events from a user's GPS trajectory, such as waiting in traffic, at an intersection, or at a bus stop. In this paper, we develop and experimentally evaluate a novel algorithm that analyses a mobile user's bearing change distribution, together with speed and acceleration, to extract significant places of events from their GPS trajectory.