On the approximation of curves by line segments using dynamic programming
Communications of the ACM
Trajectory queries and octagons in moving object databases
Proceedings of the eleventh international conference on Information and knowledge management
Algorithmic issues in modeling motion
ACM Computing Surveys (CSUR)
Algorithm Design
Sampling Trajectory Streams with Spatiotemporal Criteria
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
SQUISH: an online approach for GPS trajectory compression
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
PNN query processing on compressed trajectories
Geoinformatica
TrajMetrix: a trajectory compression benchmarking framework
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Finding traffic-aware fastest paths in spatial networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
EHSTC: an enhanced method for semantic trajectory compression
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Elderly People Daily Life Monitoring Based on GPS Trajectory with Non-uniform Sampling
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
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The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.