Abstract
A new measurement method is proposed to calculate spatio-temporal trajectory similarity, which can reflect the similar degree between two moving object spatio-temporal trajectories compressed by the Maximal Bounding Boxes (MBB). Firstly, the similarity between two trajectories is replaced by the similarity of MBB sequences in respective trajectories which can dramatically decrease the storage volume of the trajectory data. Secondly, some factors affected the similar degree of MBB sequences are analyzed systematically, such as the time duration of overlap between two MBBs in different trajectories, space distance and the density of data points inside the boxes. And then, a similarity measurement formula is proposed by integrating these factors. Experiments show that the proposed measurement formula can improve the value of clustering index Dunn.
Highlights
With the integration of wireless communications and positioning technologies, massive data related to moving objects have been acquired and posed great challenges to the data mining community (Laube p. 2007; Roddick,J.F.et al.2007)
Based on the method proposed by Sigal Elnekave et al (Sigal Elnekave et al 2007a) the raw moving object trajectories can be compressed into some Minimum Bounding Boxes (MBB), it needs a new similarity measurement formula to calculate the similarity between two trajectories which are compressed into Maximal Bounding Boxes (MBB) sequences
In this article a new similarity measurement formula is proposed to calculate the similarity between two trajectories which consider the density of data point in the MBBs
Summary
With the integration of wireless communications and positioning technologies, massive data related to moving objects have been acquired and posed great challenges to the data mining community (Laube p. 2007; Roddick,J.F.et al.2007). Based on the method proposed by Sigal Elnekave et al (Sigal Elnekave et al 2007a) the raw moving object trajectories can be compressed into some Minimum Bounding Boxes (MBB), it needs a new similarity measurement formula to calculate the similarity between two trajectories which are compressed into MBB sequences. In this article a new similarity measurement formula is proposed to calculate the similarity between two trajectories which consider the density of data point in the MBBs. the MBB representation trajectories are clustered by integrated the similarity of time duration, space and motion characteristics. This paper is organized as follows: in Section 2 some related work on clustering moving object trajectories are briefly introduced; in Section 3 some factors are analyzed which impact the similarity measurement of MBB representation trajectories and integrate these factors to form a new measurement formula.
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