Abstract
Trajectory simplification has become a research hotspot since it plays a significant role in the data preprocessing, storage, and visualization of many offline and online applications, such as online maps, mobile health applications, and location-based services. Traditional heuristic-based algorithms utilize greedy strategy to reduce time cost, leading to high approximation error. An Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS) is proposed to obtain the optimal solution in this paper. Both min-# and min-ε problems are solved by the construction and regeneration of the breadth-first spanning tree and the shortest path search based on the directed acyclic graph (DAG). Although the proposed OPTTS algorithm can get optimal simplification results, it is difficult to apply in real-time services due to its high time cost. Thus, a new Online Trajectory Simplification Algorithm based on Directed Acyclic Graph (OLTS) is proposed to deal with trajectory stream. The algorithm dynamically constructs the breadth-first spanning tree, followed by real-time minimizing approximation error and real-time output. Experimental results show that OPTTS reduces the global approximation error by 82% compared to classical heuristic methods, while OLTS reduces the error by 77% and is 32% faster than the traditional online algorithm. Both OPTTS and OLTS have leading superiority and stable performance on different datasets.
Highlights
With the rapid growth of modern technologies to navigate objects’ geo-locations, geo-positioning mobile devices have accumulated a huge amount of trajectory data
Three trajectories from Mopsi, Geolife, and Movebank with 3273 points are simplified on a fixed compression rate = 10
In order to solve the problem that heuristic-based algorithms may cause high approximation error, this paper presents an Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS)
Summary
With the rapid growth of modern technologies to navigate objects’ geo-locations, geo-positioning mobile devices have accumulated a huge amount of trajectory data. The un-exploited knowledge behind trajectory data has attracted many researchers’ attention and interests. Different domains have all taken advantage of trajectory data in their own applications such as navigation applications, animal protection agencies, and air traffic control department [1]. With the development of sensor technology, position-locating equipment can acquire spot information more precisely, at a higher frequency, leading to stronger accuracy in trajectory tracking. Collection of points can sometimes cause problems with data storage, transmission, visualization, and pattern discovery. Massive trajectory data can occupy a large amount of storage space, increasing data transmission costs enormously [2] and leading visualization system to delay or even collapse. A growing concern for the trajectory simplification (TS) issue has been raised
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