Searching for similar trajectories in spatial networks

  • Authors:

  • E. Tiakas;A. N. Papadopoulos;A. Nanopoulos;Y. Manolopoulos;Dragan Stojanovic;Slobodanka Djordjevic-Kajan

  • Affiliations:

  • Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Computer Science, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia;Department of Computer Science, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2009

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Abstract

In several applications, data objects move on pre-defined spatial networks such as road segments, railways, and invisible air routes. Many of these objects exhibit similarity with respect to their traversed paths, and therefore two objects can be correlated based on their motion similarity. Useful information can be retrieved from these correlations and this knowledge can be used to define similarity classes. In this paper, we study similarity search for moving object trajectories in spatial networks. The problem poses some important challenges, since it is quite different from the case where objects are allowed to move freely in any direction without motion restrictions. New similarity measures should be employed to express similarity between two trajectories that do not necessarily share any common sub-path. We define new similarity measures based on spatial and temporal characteristics of trajectories, such that the notion of similarity in space and time is well expressed, and moreover they satisfy the metric properties. In addition, we demonstrate that similarity range queries in trajectories are efficiently supported by utilizing metric-based access methods, such as M-trees.