Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
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
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
A user similarity calculation based on the location for social network services
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Towards geosocial recommender systems
Proceedings of the 4th International Workshop on Web Intelligence & Communities
Real-time GPS track simplification algorithm for outdoor navigation of visually impaired
Journal of Network and Computer Applications
Improving route prediction through user journey detection
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Compact representation of GPS trajectories over vectorial road networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Direction-preserving trajectory simplification
Proceedings of the VLDB Endowment
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
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The increasing availabilities of GPS-enabled devices have given rise to the location-based social networking services (LBSN), in which users can record their travel experiences with GPS trajectories and share these trajectories among each other on Web communities. Usually, GPS-enabled devices record far denser points than necessary in the scenarios of GPS-trajectory-sharing. Meanwhile, these redundant points will decrease the performance of LBSN systems and even cause the Web browser crashed. Existing line simplification algorithms only focus on maintaining the shape information of a GPS trajectory while ignoring the corresponding semantic meanings a trajectory implies. In the LBSN, people want to obtain reference knowledge from other users' travel routes and try to follow a specific travel route that interests them. Therefore, the places where a user stayed, took photos, or changed moving direction greatly, etc, would be more significant than other points in presenting semantic meanings of a trajectory. In this paper, we propose a trajectory simplification algorithm (TS), which considers both the shape skeleton and the semantic meanings of a GPS trajectory. The heading change degree of a GPS point and the distance between this point and its adjacent neighbors are used to weight the importance of the point. We evaluated our approach using a new metric called normalized perpendicular distance. As a result, our method outperforms the DP (Douglas-Peuker) algorithm, which is regarded as the best one for line simplification so far.