Abstract
With the increasing usage of GPS-enabled devices which can record users’ travel experiences, moving object trajectories are collected in many applications. Raw trajectory data can be of large volume but storage is limited, and direction-based compression to preserve the skeleton of a trajectory became popular recently. In addition, real-time applications and constrained resources often require online processing of incoming data instantaneously. To address this challenge, in this paper we first investigate two approaches extended from Douglas-Peucker and Greedy Deviation algorithms respectively, which are two most popular algorithms for trajectory compression. To further improve the online computational efficiency, we propose a faster approximate algorithm with error bound guarantee named Angular-Deviation. Experimental results demonstrate it can achieve low running time to suit the most constrained computation environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartographer 10(2), 112–122 (1973)
Heckbert, P.S., Garland, M.: Survey of polygonal surface simplification algorithms. Technical report, Carnegie Mellon University (1997)
Jiang, W., Zhu, J., Xu, J., Li, Z., Zhao, P., Zhao, L.: HV: a feature based method for trajectory dataset profiling. In: Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9418, pp. 46–60. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26190-4_4
Keogh, E.J., Chu, S., Hart, D.M., Pazzani, M.J.: An online algorithm for segmenting time series. In: ICDM, pp. 289–296 (2001)
Kolesnikov, A.: Efficient online algorithms for the polygonal approximation of trajectory data. In: MDM, pp. 49–57 (2011)
Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Jurdak, R.: Bounded quadrant system: error-bounded trajectory compression on the go. In: ICDE, pp. 987–998 (2015)
Long, C., Wong, R.C., Jagadish, H.V.: Direction-preserving trajectory simplification. PVLDB 6(10), 949–960 (2013)
Long, C., Wong, R.C., Jagadish, H.V.: Trajectory simplification: on minimizing the direction-based error. PVLDB 8(1), 49–60 (2014)
Meratnia, N., Park, Y.-Y.: Spatiotemporal compression techniques for moving point objects. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 765–782. Springer, Heidelberg (2004)
Muckell, J., Hwang, J., Patil, V., Lawson, C.T., Ping, F., Ravi, S.S.: SQUISH: an online approach for GPS trajectory compression. In: COM.Geo, pp. 13:1–13:8 (2011)
Muckell, J., Olsen, P.W., Hwang, J., Lawson, C.T., Ravi, S.S.: Compression of trajectory data: a comprehensive evaluation and new approach. GeoInformatica 18(3), 435–460 (2014)
Potamias, M., Patroumpas, K., Sellis, T.K.: Sampling trajectory streams with spatiotemporal criteria. In: SSDBM, pp. 275–284 (2006)
Yang, Y., Ma, Z., Yang, Y., Nie, F., Shen, H.T.: Multitask spectral clustering by exploring intertask correlation. IEEE Trans. Cybern. 45(5), 1069–1080 (2015)
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: ACM-GIS, pp. 99–108 (2010)
Acknowledgments
This work is support in part by the Fundamental Research Funds for the Central Universities (No. ZYGX2015J058, No. ZYGX2015J055 and No. ZYGX2014Z007), and the National Nature Science Foundation of China (No. 61572108).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ke, B., Shao, J., Zhang, Y., Zhang, D., Yang, Y. (2016). An Online Approach for Direction-Based Trajectory Compression with Error Bound Guarantee. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-45814-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45813-7
Online ISBN: 978-3-319-45814-4
eBook Packages: Computer ScienceComputer Science (R0)