Next Article in Journal
SSIEGNOS: A New Asian Single Site Tropospheric Correction Model
Previous Article in Journal
An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping
Open AccessArticle

A Graph-Based Min-# and Error-Optimal Trajectory Simplification Algorithm and Its Extension towards Online Services

by 1,2, 1,*, 1 and 1,2
1
Key Laboratory of Spatial Information Precessing and Application System Technology, Institude of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(1), 19; https://doi.org/10.3390/ijgi6010019
Received: 3 October 2016 / Revised: 20 December 2016 / Accepted: 9 January 2017 / Published: 16 January 2017
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. View Full-Text
Keywords: trajectory simplification; breadth-first spanning tree; shortest path search; directed acyclic graph trajectory simplification; breadth-first spanning tree; shortest path search; directed acyclic graph
Show Figures

Graphical abstract

MDPI and ACS Style

Wu, F.; Fu, K.; Wang, Y.; Xiao, Z. A Graph-Based Min-# and Error-Optimal Trajectory Simplification Algorithm and Its Extension towards Online Services. ISPRS Int. J. Geo-Inf. 2017, 6, 19.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop