ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-4-751-2018 TRAJECTORY COMPRESSION WITH CONSTRAINTS OF ROAD NETWORKS Zhang L. 1 2 Liu M. 1 3 Long Y. 1 2 School of Geography Science, Nanjing Normal University, 210023 Nanjing, China Key Lab. of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, 210023 Nanjing, China School of Geographic Information and Tourism, Chuzhou University, 239000 Chuzhou, China 19 09 2018 XLII-4 751 755 Copyright: © 2018 L. Zhang et al. 2018 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-4/751/2018/isprs-archives-XLII-4-751-2018.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-4/751/2018/isprs-archives-XLII-4-751-2018.pdf

In recent, the trajectory data of moving objects is getting bigger and bigger, and it has become a very important part of the social big data. Its compression is an indispensable operation of data processing, and also it is the basis of the data storage, analysis and mining of moving objects. In the related research, there are two kinds of methods for the trajectory compression. One is to compress trajectory data based on its own spatial-temporal characteristics, another kind of methods is the map-matched trajectory compression. However, for offline trajectory compression, methods based on spatial-temporal characteristics do not take the road network constraints into account. If road networks are considered, the map matching is needed first, and it will greatly affect the efficiency of trajectory compression. Therefore, this paper proposes a new trajectory compression algorithm that combines the spatial-temporal characteristics from trajectories themselves and structural characteristics from road networks to improve the compression precision and efficiency.