GPS Tracklog Compression by Heading-Based Filtering

Abstract

In the era of ubiquitous computing, a great amount of location data is recorded by a wide variety of devices like GPS trackers or smartphones. The available datasets are proven to be very good ground for data mining and information extraction in general. Since the raw data usually carries a huge rate of redundancy, the first step of the process is to reduce dataset size. Our research focuses on mining valuable information from GPS tracklogs on smartphones, which environment is well known to be extremely sensitive to resource-demanding operations, therefore it is crucial to reduce input data size as much as possible. In this paper we introduce a heading-based filtering method, which is able to drastically decrease the number of GPS points needed to represent a trajectory. We evaluate and test it by simulations on a real-world dataset, Geolife Trajectories. We show that using this filter, data is reduced to an average of 39 percent of the original size per trajectory, which causes a 250% speedup in the total runtime of Douglas-Peucker line generalization algorithm.