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Trajectory Preprocessing

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Computing with Spatial Trajectories

Abstract

A spatial trajectory is a sequences of (x,y) points, each with a time stamp. This chapter discusses low-level preprocessing of trajectories. First, it discusses how to reduce the size of data required to store a trajectory, in order to save storage costs and reduce redundant data. The data reduction techniques can run in a batch mode after the data is collected or in an on-line mode as the data is collected. Part of this discussion consists of methods to measure the error introduced by the data reduction techniques. The second part of the chapter discusses methods for filtering spatial trajectories to reduce measurement noise and to estimate higher level properties of a trajectory like its speed and direction. The methods include mean and median filtering, the Kalman filter, and the particle filter.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Wang-Chien Lee

  2. Microsoft Research, Redmond, WA, 98052, USA

    John Krumm

Authors
  1. Wang-Chien Lee
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  2. John Krumm
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Correspondence to Wang-Chien Lee .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, 100080, China, People's Republic

    Yu Zheng

  2. , School of Information Technology, University of Queensland, Brisbane, 4072, Queensland, Australia

    Xiaofang Zhou

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Lee, WC., Krumm, J. (2011). Trajectory Preprocessing. In: Zheng, Y., Zhou, X. (eds) Computing with Spatial Trajectories. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1629-6_1

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  • DOI: https://doi.org/10.1007/978-1-4614-1629-6_1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-1628-9

  • Online ISBN: 978-1-4614-1629-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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