The enormous prevalence and popularity of GPS devices have led to a growth market called Location-based services. These applications, often developed for mobile devices, utilize location information, such as the location of the person accessing the application, and commonly the location patterns of other users. Location-based service is an active area of research, encompassing applications such as supply-chain management, location detection and traffic modeling.

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.